Patent Publication Number: US-2020304797-A1

Title: Cluster refinement for texture synthesis in video coding

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of International Application No. PCT/EP2018/057477, filed on Mar. 23, 2018, which claims priority to International Application No. PCT/EP2017/082072, filed on Dec. 8, 2017, and International Application No. PCT/EP2017/082071, filed on Dec. 8, 2017. The disclosures of the aforementioned applications are hereby incorporated by reference in their entireties herein. 
    
    
     FIELD 
     The present disclosure relates to image and/or video coding and decoding employing texture synthesis. 
     BACKGROUND 
     Current hybrid video codecs, such as H.264/AVC (Advanced Video Coding) or H.265/HEVC (High Efficiency Video Coding), employ compression including predictive coding. A picture of a video sequence is subdivided into blocks of pixels, and these blocks are then coded. Instead of coding a block pixel by pixel, the entire block is predicted using already encoded pixels in the spatial or temporal proximity of the block. The encoder further processes only the differences between the block and its prediction. The further processing typically includes a transformation of the block pixels into coefficients in a transformation domain. The coefficients may then be further compressed by means of quantization and further compacted by entropy coding to form a bitstream. The bitstream further includes any signaling information, which enables the decoding of the encoded video. For instance, the signaling may include settings concerning the encoding, such as size of the input picture, frame rate, quantization step indication, prediction applied to the blocks of the pictures, or the like. The coded signaling information and the coded signal are ordered within the bitstream in a manner known to both the encoder and the decoder. This enables the decoder parsing the coded signaling information and the coded signal. 
     Depending on the selected configuration, HEVC achieves a 40-60% bit rate reduction over the predecessor standard Advanced Video Coding (AVC) while maintaining the same visual quality. Although the overall coding efficiency is superior, analyses reveal that HEVC performs differently well for varying signal characteristics. The predictability of the currently coded block based on previously coded blocks is of crucial importance for a high coding efficiency because the resulting prediction error accounts for a major part of the overall bit rate. While signal parts with low complexity textures or foreground objects with distinct borders can be efficiently coded, this is not possible for signal parts with high-complexity and irregular textures. These textures are hardly predictable, neither by intra prediction nor by motion compensation. 
     SUMMARY 
     The described limitation of HEVC can be traced back to the premise of the encoding system that a high pixel-wise fidelity of the reconstructed video is a suitable indicator for a well-encoded video. However, considering the properties of the human visual system and that the viewer never saw the originally encoded video, a high pixel-wise fidelity is not imperative. Texture synthesis may be an adequate means to cope with the low efficiency of conventional coding methods for these complex textures. Instead of aiming at pixel-wise fidelity, texture synthesis algorithms target a compelling subjective quality of the reconstructed video. 
     In view of the above, the present disclosure provides an efficient encoding and/or decoding mechanism for video signal based on texture synthesis. 
     In embodiments of the present disclosure, in order to improve the texture synthesis, cluster refinement is performed for the video images. The cluster refinement is performed by polynomial fitting of the synthesizable (texture) region and determining of differences between the fitted polynomial and the synthesizable region to identify portions not belonging to the cluster. 
     According to an aspect of the present disclosure, an apparatus is provided for encoding a video image including samples. The apparatus includes a processing circuitry, which is configured to: perform clustering to identify a texture region within the video image; determine one or more brightness parameters of a polynomial by fitting the polynomial to the identified texture region; detect in the identified texture region samples with a distance to the fitted polynomial exceeding a first threshold and identify a refined texture region as the texture region excluding one or more of the detected samples; and encode the refined texture region separately from portions of the video image not belonging to the refined texture region. 
     Such refined clustering takes into account even small objects with colors similar to parts of the texture region and may provide for an improved assignment of the samples to the synthesizable and non-synthesizable regions. 
     In an exemplary implementation, the processing circuitry is further configured to evaluate the location of the detected samples and to add isolated clusters of the detected samples smaller than a second threshold to the refined texture region. 
     This additional refinement further homogenizes the texture region and the remaining regions. It may also contribute to a more precise identification of the texture regions and non-texture regions. 
     In an exemplary implementation, the processing circuitry is further configured to evaluate the location of the samples of the texture region and to exclude isolated clusters of the texture region from the refined texture region, the isolated clusters having a size exceeding a third threshold. 
     This additional refinement also further homogenizes the texture region and the remaining regions. It may contribute to a more precise identification of the texture regions and non-texture regions. 
     For example, the fitting and the detection of the samples with the distance to the fitted polynomial exceeding a distance threshold is performed at least in a luminance component. For instance, the polynomial is a plane. However, the disclosure is not limited thereto, and the polynomial may be a polynomial of a higher order at least in one of the directions (x, y). Plane fitting is computationally less complex and may still provide a good approximation of the luminance in the texture region. On the other hand, polynomials of higher order may be more precise. 
     In one exemplary implementation, the clustering is performed by the K-means technique with a feature including at least one of color component values of the respective samples and the sample coordinates. 
     For example, the encoding of the refined texture region further includes: determining a patch corresponding to an excerpt from the refined texture region and encoding the patch; determining a set of parameters for modifying the patch and encoding the set of parameters; and encoding a texture location information indicating parts of the video image which belong to the refined texture region. 
     For instance, the set of parameters includes the one or more brightness parameters. It may be advantageous for computational reasons to apply the same fitting for the purpose of luminance adjustment as well as for the purpose of clustering. 
     In one exemplary implementation, the portions of the video image not belonging to the refined texture region are encoded by an encoder applying transformation and quantization. 
     The processing circuitry may be further configured to: divide the video image into blocks; determine for each block whether or not it is synthesizable, wherein a block is determined to be synthesizable if all samples in the block belong to the refined texture region and non-synthesizable otherwise; and encode as the texture location information a bitmap which indicates for each block whether or not it is synthesizable according to the determination. 
     Discretization of the picture by larger units than samples enables a more efficient coding of the texture region location. Moreover, it harmonizes with the block-wise operation of some current codecs and may provide further advantages for parallel processing. 
     According to an aspect of the present disclosure, an apparatus is provided for decoding a video image encoded with an apparatus according to any of the above aspects or examples. The apparatus includes a processing circuitry, which is configured to: decode the refined texture region separately from portions of the video image not belonging to the refined texture region. In the decoding apparatus, the processing circuitry is, for instance, further configured to decode a texture location information indicating for each block of a video image, whether or not the block belong to the synthesizable portion including texture region. 
     According to another aspect of the present disclosure, a method is provided for encoding a video image including samples. The method includes: performing clustering to identify a texture region within the video image; determining one or more brightness parameters of a polynomial by fitting the polynomial to the identified texture region; detecting in the identified texture region samples with a distance to the fitted polynomial exceeding a first threshold and identifying a refined texture region as the texture region excluding one or more of the detected samples; and encoding the refined texture region separately from portions of the video image not belonging to the refined texture region. 
     The method may further comprise evaluating the location of the detected samples and adding isolated clusters of the detected samples smaller than a second threshold to the refined texture region. The method may further include evaluating the location of the samples of the texture region and excluding isolated clusters of the texture region from the refined texture region, the isolated clusters having a size exceeding a third threshold. 
     For example, the fitting and the detection of the samples with the distance to the fitted polynomial exceeding a distance threshold is performed at least in luminance component. 
     In an implementation form, the polynomial is a plane. 
     Moreover, the clustering may be performed by the K-means technique with a feature including at least one of color component values of the respective samples and the sample coordinates. 
     The encoding of the refined texture region can further include: determining a patch corresponding to an excerpt from the refined texture region and encoding the patch; determining a set of parameters for modifying the patch and encoding the set of parameters; and encoding a texture location information indicating parts of the video image which belong to the refined texture region. 
     For example, the set of parameters includes the one or more brightness parameters. 
     In one exemplary implementation, the portions of the video image not belonging to the refined texture region are encoded by an encoder applying transformation and quantization. 
     The method may further comprise: dividing the video image into blocks; determining for each block whether or not it is synthesizable, wherein a block is determined to be synthesizable if all samples in the block belong to the refined texture region and non-synthesizable otherwise; and encoding as the texture location information a bitmap which indicates for each block whether or not it is synthesizable according to the determination. 
     According to an aspect of the present disclosure, a decoding method is provided for decoding a video image encoded with a method as described above. The decoding method includes: decoding the refined texture region separately from portions of the video image not belonging to the refined texture region. The decoding method may comprise decoding a texture location information indicating for each block of a video image whether or not the block belong to the synthesizable portion including texture region. 
     According to an aspect of the present disclosure a non-transitory computer-readable storage medium is provided storing instructions which when executed by a processor/processing circuitry perform the steps according to any of the above aspects or embodiments or their combinations. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the following, exemplary embodiments are described in more detail with reference to the attached figures and drawings, in which: 
         FIG. 1  is a block diagram illustrating an encoder supporting both texture coding and hybrid coding; 
         FIG. 2  is a block diagram illustrating a decoder supporting both texture decoding and hybrid decoding; 
         FIG. 3  is a schematic drawing illustrating functionality of an encoder supporting both texture coding and hybrid coding; 
         FIG. 4  is a schematic drawing illustrating functionality of a decoder supporting both texture decoding and hybrid decoding; 
         FIG. 5  is a schematic drawing illustrated image portions in different stages of the texture region reconstruction; 
         FIG. 6A  is a block diagram illustrating a hybrid encoder; 
         FIG. 6B  is a block diagram illustrating the hybrid encoder with alternative texture encoder; 
         FIG. 7A  is a block diagram illustrating a hybrid decoder; 
         FIG. 7B  is a block diagram illustrating the hybrid decoder with alternative texture decoder; 
         FIG. 8  is a schematic drawing illustrating processing for detection of synthesizable regions in an image; 
         FIG. 9  is a schematic drawing illustrating processing for motion compensation of synthesizable regions in an image; 
         FIG. 10  is a schematic drawing illustrating processing for luminance adjustment of synthesizable regions in an image; 
         FIG. 11  is a schematic drawing illustrating processing for frequency adjustment of synthesizable regions in an image; 
         FIG. 12  is a schematic drawing illustrating processing results in different stages of luminance adjustment processing; 
         FIG. 13  is a schematic drawing illustrating processing results in different stages of frequency adjustment processing; 
         FIG. 14  is a schematic drawing illustrating signaling information generated at the encoder applying texture coding; 
         FIG. 15  is a schematic drawing illustrating signaling information utilized at the decoder applying texture decoding; 
         FIG. 16  is a schematic drawing of a pipeline for a combined HEVC and textured decoder; 
         FIG. 17  is a schematic drawing illustrating fitting of a plane on a synthesizable image region; 
         FIG. 18  is a schematic drawing illustrating calculating of differences between the samples of the synthesizable region and the fitted plane; 
         FIG. 19  is an illustration of an exemplary distance map calculated between the samples of the synthesizable region and the fitted plane; 
         FIG. 20  is an illustration of an exemplary image to be clustered and the result of synthesizable region detection by K-means algorithm; 
         FIG. 21  is an illustration of an exemplary result of synthesizable region detection by K-means algorithm in comparison with the result in which some further samples are marked as non-synthesizable based on a comparison with the fitted plane; 
         FIG. 22  is an illustration of an exemplary result in which some further samples are marked as non-synthesizable based on a comparison with the fitted plane in comparison with a result of cluster refinement; and 
         FIG. 23  is a schematic drawing illustrating processing for detection and refinement of synthesizable regions in an image. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, reference is made to the accompanying figures, which form part of the disclosure, and which show, by way of illustration, exemplary aspects of embodiments of the present disclosure or exemplary aspects in which embodiments of the present disclosure may be used. It is understood that embodiments of the present disclosure may be used in other aspects and comprise structural or logical changes not depicted in the figures. The following detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present disclosure is defined by the appended claims. 
     For instance, it is understood that a disclosure in connection with a described method may also hold true for a corresponding device or system configured to perform the method and vice versa. For example, if one or a plurality of specific method steps are described, a corresponding device may include one or a plurality of units, e.g. functional units, to perform the described one or plurality of method steps (e.g. one unit performing the one or plurality of steps, or a plurality of units each performing one or more of the plurality of steps), even if such one or more units are not explicitly described or illustrated in the figures. On the other hand, for example, if a specific apparatus is described based on one or a plurality of units, e.g. functional units, a corresponding method may include one step to perform the functionality of the one or plurality of units (e.g. one step performing the functionality of the one or plurality of units, or a plurality of steps each performing the functionality of one or more of the plurality of units), even if such one or plurality of steps are not explicitly described or illustrated in the figures. Further, it is understood that the features of the various exemplary embodiments and/or aspects described herein may be combined with each other, unless specifically noted otherwise. 
     Some early work on using texture synthesis for video coding is presented in P Ndjiki-Nya, B. Makai, A. Smolic, H. Schwarz and T Wiegand, “ Video coding using Texture Analysis and synthesis ” in  PCS,  2003. At the encoder side, textures are semi-automatically classified into regions with relevant and irrelevant subjective details. Similar statistical properties are used to find the homogenous regions. Inhomogeneous blocks are split further, while homogeneous blocks remain unchanged. The segmentation mask obtained after the splitting step typically shows a clearly over-segmented frame. Thus post-processing of the former is required, which leads to the second step implemented by the texture analyzer—the merging step. For that, homogeneous blocks identified in the splitting step are compared pairwise and similar blocks are merged into a single cluster forming a homogeneous block itself. The merging stops if the obtained clusters are stable. The similarity assessment between two blocks is done based on MPEG-7 descriptors, edge histogram and color histogram. 
     A work presented in A. Dumitras and B. G. Haskell, “A texture Replacement Method at the Encoder for Bit-Rate Reduction of Compressed Video,” IEEE Trans. Circuits Syst. Video Techn., vol. 13, no. 2, 2003 proposes an encoder in which the original texture is removed from the selected regions of the original frames. The removed texture is analyzed. Using the resulting texture parameters and a set of constraints, new texture is then synthesized. The region segmentation and texture removal steps identify all of the pixels in the original frame that have similar color characteristics to the color characteristics of the pixels in their surroundings or some pixels selected by the user, and replace the texture in the segmented regions. The color characteristics are evaluated using an angular map and a modulus map of the color vectors in the RGB space. 
     When reviewing the state of the art, some disadvantages have been discovered by the Inventors. The above mentioned works achieve some plausible results for video sequences presented by the authors. However, these are mostly simple sequences not including several challenging events, for instance lighting and frequency changes of textures. They do not consider more complex camera motions like tilting and zooming, either. Reconstructing lighting changes was tried by some authors. They use information of neighboring pixels to reconstruct synthesizable regions. This allows for a plausible luminance reconstruction at the edges but cannot reconstruct lighting gradients reasonably well. Therefore, it is not well suited for larger areas. Moreover, most state-of-the-art approaches that deal with the texture coding merely state that a picture can be decomposed into synthesizable and non-synthesizable regions. However, a detailed description of the decomposition is rarely given or the decomposition is assumed to be known or not fully automatic and reliable but rather user-assisted. In other words, only a small amount of known approaches employ an automatic region detection for synthesizable regions, while they do not show how to deal with errors during the cluster detection. 
     The present disclosure provides an approach for providing a refined clustering (identification of synthesizable and non-synthesizable regions), reconstructing the texture, motion, luminance gradients and frequency components by using a relatively small set of variables. 
     As mentioned above, texture synthesis is an adequate procedure to cope with the low efficiency of conventional coding methods for these complex textures. Instead of aiming at pixel-wise fidelity, texture synthesis algorithms target a compelling subjective quality of the reconstructed video. 
     For this reason, the encoded video is segmented into synthesizable and non-synthesizable regions. Subsequently, texture synthesis is used to reconstruct the synthesizable regions. The remaining parts of the signal are encoded conventionally. Thereby, the bit rate costs for the synthesizable regions may be substantially reduced and, in addition, these regions may be reconstructed with a high subjective quality. Furthermore, the released bit rate resources can be reallocated to the conventionally encoded signal. Hence, the quality of these signal parts can be increased while maintaining the same overall bit rate. 
     One of the important prerequisites to perform texture coding is identification of the texture region to be synthesized. The present disclosure provides an efficient approach to identify the clusters in the image, i.e. to set or define the synthesizable and non-synthesizable regions. 
     The corresponding encoder  100  for texture coding is illustrated in  FIG. 1 .  FIG. 1  shows an input image entering analysis and decomposition unit  110 . The input image may be an image of a video sequence. However, it is noted that in general, the present disclosure is also applicable for still images. In the analysis and decomposition unit  110 , the image is analyzed and segmented into synthesizable regions and non-synthesizable regions. For instance, the image is subdivided into blocks of the same or variable size and for each block, a decision is made on whether the block belongs to the synthesizable region or non-synthesizable region. The synthesizable regions are provided to the texture analysis unit  120  in which these regions are parametrized. For instance, each block is described by means of a set of parameters necessary for their reconstruction. There texture parameters are binarized and output from the texture analysis unit  120  to a multiplexer  140 , which inserts them into the bitstream. The insertion into the bitstream obeys a predefined bitstream syntax and semantics known to both the encoder and the decoder so that a decoder is able to parse the bitstream into the syntax elements again, and give them appropriate meaning according to the semantics. 
     The non-synthesizable regions are input to the HEVC/AVC encoder  130  for conventional image or video coding. It is noted that the HEVC/AVC encoder is only an example of a conventional encoder. In general, any image or video encoder can be employed, lossy or lossless. The HEVC/AVC encoder  130  generates portions of the bitstream, which are multiplexed with the portions of the bitstream generated by the texture analysis unit  120 . It is noted that, in general, two separate bitstreams may be generated so that the multiplexing does not have to take place. 
     Correspondingly,  FIG. 2  shows an exemplary decoder  200 , which is capable of decoding the bitstream generated by the encoder of  FIG. 1 . The bitstream including both parts—coded by means of HEVC/AVC encoder  130  and by the texture analysis unit  120 —is input to the demultiplexer  240 . The demultiplexer divides the bitstream into the texture parameter portion, which is input to a texture synthesis unit  220  and into the HEVC/AVC portion, which is input to the HEVC/AVC decoder  230 . The texture synthesis unit  220  uses the texture parameters to synthesize the texture, and outputs the synthesized texture regions to a reconstruction unit  210 , which combines the synthesized regions with regions decoded with a conventional decoder  230 . Correspondingly, the HEVC/AVC decoder  230  (the conventional decoder) decodes the portion of the bitstream input from the demultiplexer  240  and provides the decoded regions to the reconstruction unit  210 . The reconstruction unit combines both region types synthesized regions and conventionally decoded regions to form output image. 
     In this disclosure, the term image refers to a digital image that is a two-dimensional matrix including samples of pixel brightness values of one or more component colors. For instance, an image may be a greyscale image including N×M samples, each of the samples having a value ranging from 0 to 255 grey levels (corresponding to 8 bits per value) or 0 to 1023 gray levels (corresponding to 10 bits per value) or a different range (corresponding to more or less bits per value, or corresponding to definition in a standard such as ITU-R BT. 2020). Alternatively, an image may be a color image including samples of three color components such as red, green, blue colors, each sample of each color may take a value ranging from 0 to 255 levels. However, the present disclosure is not limited to any particular color space, and in general any color space such as YUV, YCbCr, or the like, possibly using sub sampling of color components (in spatial domain for instance by only taking every second pixel) may be used, as is known to those skilled in the art. In addition, instead of the aforementioned 8 bit grey level or color depth, other grey level or color depth quantizations may be used, e.g. a 10 bit quantization or any other higher or lower bit number quantization. The term “image” here is used as synonymously with the term “picture”. The term “frame” refers to an image or a picture which is a part of a video sequence, i.e. a video frame, used synonymously with “video picture”. 
     The term “video” here refers to a sequence of images capturing a scene or a plurality of scenes. This term is used synonymously with the term “motion picture”. Typically, the frames of the video are captured by a camera with other predefined temporal resolution of for instance, 25, 30, 60, or the like, frames per second. However, the present disclosure is not limited to natural video sequences. Alternatively, a video may be generated by computer graphics and/or animation. 
     The term “pixel” means one or more samples defining the brightness and/or color. Accordingly, a pixel may consist of a single sample defining for instance luminance. However, a pixel may also include samples corresponding to different colors, such as red, green, blue, or a luminance and respective chrominances, depending on the color space employed. 
     Simply replacing a synthesizable region by a synthesized texture results in three major issues:
         1) The synthesized texture for subsequent frames needs to be consistent, i.e. camera motion has to be compensated.   2) Luminance information, perspective effects, and blurring may be lost when reconstructing the texture from a single small patch.   3) Block artifacts between synthesized and non-synthesized regions may result in poor subjective quality of the reconstructed video.       

     Moreover, wrong clustering into synthesizable regions causes efficiency losses in the texture based coding. In some embodiments shown in the present disclosure, a complete pipeline for texture analysis and synthesis is provided. In particular, a sophisticated decomposing technique also suitable for high quality sports video broadcasting application is provided, which may provide improved results in terms of bit rate savings and subjective quality, respectively. This is achieved by a cluster refinement technique based on distances to a polynomial fitted to the luminance channel. 
     Some of the above mentioned issues are solved by the texture synthesis solution of the present disclosure. In particular, the present disclosure provides a possibility of frequency damping, e.g. by higher-order polynomial fitting, which may compensate for perspective effects and motion blur. This addresses especially the above mentioned issue 2). 
     In addition, or in alternative embodiments, motion compensation, e.g. by using hyperplane fitting, may be applied. Another beneficial tool may be luminance reconstruction employing polynomial fitting and/or a deblocking method to reduce block artifacts between synthesized and non-synthesized regions by applying a mincut algorithm to neighboring blocks at the region borders. 
       FIG. 3  illustrates functional aspects of the encoder  100  of  FIG. 1 . In particular, an encoder  300  according to an embodiment of the present disclosure receives an input video frame  301  and decomposes  310  the video frame into a synthesizable portion (texture)  320  and a non-synthesizable remaining portion (rest)  330 . Moreover, a control information concerning the decomposition  340  is provided as an output of the encoder. For instance, a block map, indicating for each block of the input video frame whether it is pertaining to a synthesizable or non-synthesizable region, is output. For the synthesizable regions, a representative patch  350  may be further output. The non-synthesizable region  330  is then encoded  360  with a conventional encoder. The conventional encoding  360  in this example is HEVC coding, which results into HEVC bitstream on the output. The texture  320  is then analyzed, which means that based on the representative patch and predefined operations with adjustable parameters each texture block is approximated. In other words, for the given patch and the given texture block parameters for the predefined operations are selected to minimize the difference between the texture block and that patch. In  FIG. 3 , the predefined operations include motion compensation  370 , luminance coding  380 , and frequency adjustment  390 . The parameters of these three operations are then output. The output HEVC bitstream, texture parameters, patch, and block map, are stored and/or transmitted and form the encoded video. For instance, the encoded video may be stored and/or transmitted in form of a bitstream including the HEVC bitstream, and binarized texture parameters, patch, and block map. 
       FIG. 4  illustrates a decoder  400  capable of decoding the encoded video output from the encoder  300  described above with reference to  FIG. 3 . In particular, the above-mentioned parameters including HEVC bitstream, texture parameters, patch, and block map are parsed/decoded from data retrieved from a storage or received from a channel. The HEVC bitstream is input to HEVC decoder  460  which performs decoding according to the HEVC standard and output the decoded non-synthesizable regions  430 . The texture parameters and the patch information  450  are input to a synthesis  470 , which synthesizes texture based on the patch by applying to the patch the motion compensation, the luminance coding and the frequency adjustment according to the parameters input. The resulting texture  420  together with the non-synthesizable portion  430  are combined in a composition unit  410  in accordance with the received block map  440  to obtain reconstructed video frame  401 . 
     The present disclosure makes use of the idea that an image  301  can be decomposed  310  in textured  320  and non-textured  330  regions. By selecting a small image patch  350  most important structural information of the textured region  320  can be represented. Using patch-based texture synthesis algorithms  470 , the structural information of the region  420  is reconstructed from this patch  450  in the decoder  400 . Because lighting and blurring information is lost when simply replacing the region with a synthesized image, these are signaled as a sparse representation, for instance, in a slice header. For image sequences the synthesis only needs to be done a single time for a tracked region of similar texture. 
     In this disclosure, the term “texture” refers to an image portion (including one or more color components) and includes information about the spatial arrangement of color or intensities in the image portion. The image portion normally exhibits spatial homogeneity or sequences of images of moving scenes that exhibit certain stationarity properties in time. See, e.g., U. S. Thakur, K. Naser, M. Wien, “Dynamic texture synthesis using linear phase shift interpolation,” PCS 2016, December, 2016. 
       FIG. 5  illustrates various partial results during the image reconstruction as shown in  FIG. 4 . In particular, a patch  550  is used to generate a texture image  510  by simply copying the patch to the entire image area. Then, the synthesized texture  510  is further adapted, for instance as mentioned above by reconstructing the motion, luminance and frequency. The resulting image  520  obtained after the adjustment(s) is then combined with the conventionally coded image  530  with the blackened portions corresponding to the textured area. The combined decoded image  500  includes both the reconstructed textured (synthesizable) region and the non-synthesizable (rest) region. 
       FIG. 6A  shows an overview of the conventional encoder  605  applying hybrid coding, which means encoding including a plurality of encoding steps or stages such as prediction  620 , transformation  630 , quantization  640 , and entropy coding  650 . It is noted that conventional coders such as AVC or HEVC also employ hybrid coding. 
     In the following, the HEVC encoding and decoding is briefly described. HEVC stands for High-Efficiency Video Coding and is follower of the AVC (H.264) video coding standard. 
     An encoder  605  comprises an input for receiving input image samples of frames or pictures of a video stream and an output for generating an encoded video bitstream. The term “frame” in this disclosure is used as a synonym for picture. However, it is noted that the present disclosure is also applicable to fields in case interlacing is applied. In general, a picture includes m times n pixels. This corresponds to image samples, and may comprise one or more color components. For the sake of simplicity, the following description refers to pixels meaning samples of luminance. However, it is noted that the motion vector search of the present disclosure can be applied to any color component, including chrominance or components of a search space such as RGB or the like. On the other hand, it may be beneficial to only perform motion vector estimation for one component and to apply the determined motion vector to more (or all) components. 
     The input blocks (also referred to sometimes as coding units, CU, or processing units, PU) to be coded do not necessarily have the same size. CU is a basic coding structure of the video sequence of a pre-defined size, containing a part of a picture (e.g., 64×64 pixels). It is usually of regular, rectangular shape, describing encoded area of the picture using syntax specified for a coding mode selected for the block. 
     One picture may include blocks of different sizes and the block raster of different pictures may also differ. In an explicative realization, the encoder  605  is configured to apply prediction, transformation, quantization, and entropy coding to the video stream. The transformation, quantization, and entropy coding are carried out respectively by a transform unit  630 , a quantization unit  640  and an entropy encoding unit  650  so as to generate as an output the encoded video bitstream. 
     The video stream may include a plurality of frames. Each frame is divided into blocks of a certain size that are either intra- or inter-coded. The blocks of, for example the first frame of the video stream are intra coded by means of an intra prediction unit, which may be part of the prediction unit  620 . An intra frame is coded using only the information within the same frame, so that it can be independently decoded, and it can provide an entry point in the bitstream for random access. Blocks of other frames of the video stream may be inter coded by means of an inter prediction unit, which may be part of the prediction unit  620 . Information from previously coded frames (reference frames) is used to reduce the temporal redundancy, so that each block of an inter-coded frame is predicted from a block in a reference frame. A mode selection unit, which may also be part of the prediction unit  620 , is configured to select whether a block of a frame is to be processed by the intra prediction unit or the inter prediction unit. This mode selection unit also controls the parameters of intra or inter prediction. In order to enable refreshing of the image information, intra-coded blocks may be provided within inter-coded frames. Moreover, intra-frames which contain only intra-coded blocks may be regularly inserted into the video sequence in order to provide entry points for decoding, i.e. points where the decoder can start decoding without having information from the previously coded frames. 
     The intra estimation unit and the intra prediction unit are units that perform the intra prediction. In particular, the intra estimation unit may derive the prediction mode based also on the knowledge of the original image, while intra prediction unit provides the corresponding predictor, i.e. samples predicted using the selected prediction mode, for the difference coding. For performing spatial or temporal prediction, the coded blocks may be further processed by an inverse quantization unit  660 , and an inverse transform unit  670 . After reconstruction of the block a loop filtering may be applied to further improve the quality of the decoded image. The filtered blocks then form the reference frames that are then stored in a decoded picture buffer. Such decoding loop (decoder) at the encoder side provides the advantage of producing reference frames, which are the same as the reference pictures reconstructed at the decoder side. Accordingly, the encoder and decoder side operate in a corresponding manner. The term “reconstruction” here refers to obtaining the reconstructed block by adding to the decoded residual block the prediction block. 
     The inter estimation unit receives as an input a block of a current frame or picture to be inter coded and one or several reference frames from the decoded picture buffer. Motion estimation is performed by the inter estimation unit whereas motion compensation is applied by the inter prediction unit. The motion estimation is used to obtain a motion vector and a reference frame based on certain cost function, for instance using also the original image to be coded. For example, the motion estimation unit may provide initial motion vector estimation. The initial motion vector may then be signaled within the bitstream in form of the vector directly or as an index referring to a motion vector candidate within a list of candidates constructed based on a predetermined rule in the same way at the encoder and the decoder. The motion compensation then derives a predictor of the current block as a translation of a block co-located with the current block in the reference frame to the reference block in the reference frame, i.e. by a motion vector. The inter prediction unit outputs the prediction block for the current block, wherein the prediction block minimizes the cost function. For instance, the cost function may be a difference between the current block to be coded and its prediction block, i.e. the cost function minimizes the residual block. The minimization of the residual block is based, e.g., on calculating a sum of absolute differences (SAD) between all pixels (samples) of the current block and the candidate block in the candidate reference picture. However, in general, any other similarity metric may be employed, such as mean square error (MSE) or structural similarity metric (SSIM). 
     However, a cost-function may also be the number of bits necessary to code such inter-block and/or distortion resulting from such coding. Thus, the rate-distortion optimization procedure may be used to decide on the motion vector selection and/or in general on the encoding parameters such as whether to use inter or intra prediction for a block and with which settings. 
     The intra estimation unit and inter prediction unit receive as an input a block of a current frame or picture to be intra coded and one or several reference samples from an already reconstructed area of the current frame. The intra prediction then describes pixels of a current block of the current frame in terms of a function of reference samples of the current frame. The intra prediction unit outputs a prediction block for the current block, wherein the prediction block advantageously minimizes the difference between the current block to be coded and its prediction block, i.e., it minimizes the residual block. The minimization of the residual block can be based, e.g., on a rate-distortion optimization procedure. In particular, the prediction block is obtained as a directional interpolation of the reference samples. The direction may be determined by the rate-distortion optimization and/or by calculating a similarity measure as mentioned above in connection with inter-prediction. 
     The inter estimation unit receives as an input a block or a more universal-formed image sample of a current frame or picture to be inter coded and two or more already decoded pictures. The inter prediction then describes a current image sample of the current frame in terms of motion vectors to reference image samples of the reference pictures. The inter prediction unit outputs one or more motion vectors for the current image sample, wherein the reference image samples pointed to by the motion vectors advantageously minimize the difference between the current image sample to be coded and its reference image samples, i.e., it minimizes the residual image sample. The predictor for the current block is then provided by the inter prediction unit for the difference coding. 
     The difference between the current block and its prediction, i.e. the residual block, is then transformed by the transform unit  630 . The transform coefficients are quantized by the quantization unit  640  and entropy coded by the entropy encoding unit  650 . The thus generated encoded picture data, i.e. encoded video bitstream, comprises intra coded blocks and inter coded blocks and the corresponding signaling (such as the mode indication, indication of the motion vector, and/or intra-prediction direction). The transform unit  630  may apply a linear transformation, such as a Fourier or Discrete Cosine Transformation (DFT/FFT or DCT). Such transformation into the spatial frequency domain provides the advantage that the resulting coefficients have typically higher values in the lower frequencies. Thus, after an effective coefficient scanning (such as zig-zag), and quantization, the resulting sequence of values has typically some larger values at the beginning and ends with a run of zeros. This enables further efficient coding. Quantization unit  640  performs the actual lossy compression by reducing the resolution of the coefficient values. 
     The entropy coding unit  650  then assigns to coefficient values binary codewords to produce a bitstream. The entropy coding unit  650  also codes (generates syntax element value and binarizes it) the signaling information. Variable length coding or fixed length coding is applied to some syntax elements. In particular, context-adaptive binary arithmetic coding (CABAC) may be used. 
     It is noted that the bitstream is organized based on the syntax defined by the standard. For example, blocks are grouped into slices that are individually decodable, i.e. do not depend from other slices in the same picture. The compressed video samples of the blocks within the slices are typically preceded by control (signaling information) referred to as a slice header. This control information carries parameters common for encoding/decoding the blocks within the slice. Moreover, SPS (Sequence Parameter Set) and PPS (Picture Parameter Set) are portions of the bitstream (containers) carrying control information, which is relevant for one or more frames or for the entire video. Video sequence in this sense is a set of subsequent frames presenting motion picture. In particular, the SPS in HEVC is set of parameters sent in form of organized message containing basic information required to properly decode the video stream; must be signaled at the beginning of every random access point. PPS is a set of parameters sent in form of organized message containing basic information required to properly decode a picture in the video sequence. 
       FIG. 6B  shows an overview of a conventional encoder supplemented with encoder based on texture analysis  690 . As can be seen in  FIG. 6B , the input video is first subdivided into synthesizable portion and non-synthesizable portion. The synthesizable portion is provided for the texture analysis  690 , which outputs texture parameters for the bitstream as described above with reference to  FIG. 3 . 
       FIG. 7A  shows an overview of the conventional decoder  700  capable of decoding a bitstream generated by a conventional encoder  605  is described with reference to  FIG. 6A . In particular, the conventional decoder  700  receives a bitstream as an import performance entropy decoding  710  resulting in quantised transfer coefficients and control information. The quantised transform coefficients out the quantised  720 , inverse transformed  730 , and the resulting residual block is provided for reconstruction. The control information controls the prediction module  740 , which generates prediction block to be combined with the residual block in the reconstruction unit  750  to obtain the reconstructed block of the image. This approach is repeated for all blocks of the image. 
     Similarly, HEVC video decoding is visualized in  FIG. 7A .  FIG. 7A  shows a video decoder  700 . The video decoder  700  comprises particularly a decoded picture buffer, an inter prediction unit and an intra prediction unit, which form a block prediction unit  740 . The decoded picture buffer is configured to store at least one (for uni-prediction) or at least two (for bi-prediction) reference frames reconstructed from the encoded video bitstream. The reference frames are different from a current frame (currently decoded frame) of the encoded video bitstream. The intra prediction unit is configured to generate a prediction block, which is an estimate of the block to be decoded. The intra prediction unit is configured to generate this prediction based on reference samples that are obtained from the decoded picture buffer. 
     The decoder  700  is configured to decode the encoded video bitstream generated by the video encoder  605 , and preferably both the decoder  700  and the encoder  605  generate identical predictions for the respective block to be encoded/decoded. The features of the decoded picture buffer and the intra prediction unit are similar to the features of the decoded picture buffer and the intra prediction unit of  FIG. 6 . 
     The video decoder  700  comprises further units that are also present in the video encoder  605 , like e.g. an inverse quantization unit  720 , an inverse transform unit  730 , and a loop filtering, which respectively correspond to the inverse quantization unit  720 , the inverse transform unit  730 , and the loop filtering of the video coder  605 . 
     An entropy decoding unit  710  is configured to decode the received encoded video bitstream and to correspondingly obtain quantized residual transform coefficients and signaling information. The quantized residual transform coefficients are fed to the inverse quantization unit  720  and an inverse transform unit  730  to generate a residual block. The residual block is added in reconstruction unit  750  to a prediction block and the addition is fed to the loop filtering to obtain the decoded video. Frames of the decoded video can be stored in the decoded picture buffer and serve as a decoded picture for inter prediction. The entropy decoding unit  710  may correspond to the decoder which parses from the bitstream the signal samples as well as the syntax element values and then maps the corresponding control information content based on a semantic rule. 
       FIG. 7B  shows an overview of a conventional decoder supplemented with the decoder based on texture analysis  760 . In particular the texture synthesis  760  receives the texture parameters (including the patch information) and synthesizes the texture block. The texture block is provided for reconstruction which in this case merely means inserting the synthesized texture block onto the appropriate place in the image for instance according to the block map also obtained from the bitstream by the entropy decoder  710 . 
     In the following, a detailed description of embodiments of the present disclosure concerning different parts of textured analysis and synthesis are described. 
     In particular, the texture analysis performed at the encoder includes detection and tracking of the texture region or more regions, extracting a representative texture patch, determine adjustment parameters for adjusting the patch-based synthesized region on a frame or block basis, and signal the location of the texture region, the patch and the adjustment parameters for the decoder. The decoding involves extracting the signaled information, reconstructing the texture based on the signaled patch and the adjustment parameters and combined the reconstructed texture with the remaining image based on the signaled location information. 
     Region Detection 
     In accordance to an embodiment, the processing circuitry implementing texture region coding is configured to: detect the texture region within a video frame by using clustering; generate texture region information indicating the location of the texture region within the video frame; and insert the texture region information into the bitstream. The clustering may be performed by any known approach capable of identifying image portions of a similar character. In general, the texture region may also be detected by means other than clustering, such as a trained neural network with or without having a priori knowledge of the texture&#39;s properties, block-based feature extraction and classification of the blocks as texture when the extracted features fulfill certain conditions. 
       FIG. 3  shows decomposition unit  310  which performs region detection.  FIG. 8  shows a pipeline of the clustering and patch extraction performed by the decomposition unit  310 . In particular, for detection of a synthesizable region, the input image  801  is first clustered  810  into different regions with similar texture. In one embodiment, this is done by applying a K-means clustering, where the feature vector for each pixel consists of the three color values and the image coordinates. This five-dimensional vector not only enforces similar color, but also spatial proximity. K-means clustering is described, for instance, in MacQueen, J. B. (1967).  Some Methods for classification and Analysis of Multivariate Observations. Proceedings of  5 th Berkeley Symposium on Mathematical Statistics and Probability.  1.  University of California Press . pp. 281-297. 
     Other clustering and classification methods are conceivable. The present disclosure is not limited with respect to any particular clustering and classification. It is also noted that a pixel may include one or more color values and is not limited to the above exemplified three values. For instance, there may be color spaces including three color components such as red, green, blue, and white. Moreover, the embodiments of the present disclosure are also applicable to greyscale images. 
       FIG. 8  shows the clustering  810  and patch extraction step  820 . The upper part shows the image getting split into the clustering information  830  and the remaining image  840  using the clustering algorithm (e.g., K-means). In this embodiment the cluster information  830  may be written into a text file. The clustering information may be a bitmap, of which each bit represents an image pixel and indicates whether or not the image pixel belongs to the texture cluster or to the remaining image. Alternatively, the clustering may be performed on a block basis, which means that for each block the decision on whether or not the block belongs to a texture region or to the remaining image region is to be made. Correspondingly, the cluster information may be represented by a bitmap in which each bit represents whether or not the respective block of input image belongs to the synthesizable regions (texture) or to non-synthesizable regions (remaining image). 
     The remaining region  840  consists of the original image where all pixel values in regions suitable for texture synthesis are set to black. The black color is merely exemplary and means that the pixel sample values are set to 0. However, the present disclosure is not limited to this kind of marking the texture region. For some kinds of encoding it may be beneficial to replace the texture portion by interpolating them from the surrounding pixel values. As an alternative solution, the texture portion might be forced to be coded such as skip mode of HEVC. 
     The lower part of  FIG. 8  shows the patch extraction step  820 , which copies a small part of a region  850  detected as synthesizable. In this embodiment, the patch  850  has a size of 64×64 pixels. It is noted that the size of the patch may differ from this example. Accordingly, a smaller or larger size may be selected. A larger size of the patch provides greater variance of the texture. On the other hand, transmission of a larger patch requires more rate. It is noted that the patch does not have to have a square shape. It may be a rectangle with a size of n×m, with n and m being non-zero integers differing from each other. In other words, the present disclosure is not limited to any form or size of the patch. 
     The present disclosure is not limited to employing one single patch. Alternatively, one or more patches may be identified and provided to the decoder for reconstruction of one texture region, possibly with the information which of the patches is to be applied or written information concerning weights to combine the patches. 
     The cluster information may be further compressed, for instance, by bitmap compression approaches including lossless compression, such as a run length coding, or other approaches known from facsimile compression. However, the cluster information may also be inserted into the bitstream uncompressed. Similarly, the patch  850  may be provided in an uncompressed form or further compacted by employing any variable length coding such as Huffman coding, arithmetic coding, or the like. 
     According to the present disclosure, the decomposition into synthesizable and non-synthesizable regions is refined by calculating differences of luminance and color channels to a polynomial. A meaningful decomposition may directly result in a better subjective quality. 
     In particular, any common clustering technique is used to classify samples of the (video) image and find a texture region. Then, the found texture region is refined. In particular, a plane is fitted to the luminance values of the samples in the found texture region. For each of the samples in the texture region, a distance is calculated between the respective sample and the fitted plane surface. A threshold for the distance is defined to distinguish between correctly and wrongly clustered samples. The correctly clustered samples are samples with the distance smaller than the threshold, whereas the wrongly clustered samples are those with the difference equal to or greater than the threshold. 
     According to an embodiment, an apparatus is provided for encoding a video image including samples. the apparatus includes a processing circuitry. The processing circuitry is configured to:
         perform clustering to identify a texture region within the video image;   determine one or more brightness parameters (such as a0, a1, and a2) of a polynomial by fitting the polynomial (such as a 2D polynomial of order one or higher) to the identified texture region (in particular, to the brightness or luminance and/or chrominance of the identified texture region);   detect in the identified texture region samples with a distance to the fitted polynomial exceeding a first threshold;   identify a refined texture region as the texture region excluding one or more (e.g. also all) of the detected samples; and   encode the refined texture region separately from portions of the video image not belonging to the texture region.       

     The apparatus is configured to refine the texture region such that the refined texture region does not comprise one or more or all of the detected samples. The apparatus may further be configured to assign these one or more or all detected samples to the non-synthesizable regions. 
     The processing circuitry may include one or more hardware or software components, and it may also perform further functions of the texture coding as mentioned above, as well as functions of the hybrid coding to be performed on the non-synthesizable regions. 
     The above refinement of clustering is performed as a part of decomposition in  FIG. 3  and, more particularly, within the clustering  810  in  FIG. 8 . When looking at  FIGS. 14 and 15 , the clustering and its refinement as described above is performed in the functional unit denoted as “Blocks” in  FIG. 14  and the corresponding decoding of the indication of the synthesizable regions is performed in the functional unit with reference  1510  in  FIG. 15 . 
     In one example, the clustering is performed by the K-means technique with feature including at least one of color component values of the respective samples and the sample coordinates. The K-means technique is described in detail, for instance, in Lloyd, Stuart P (1982), “ Least squares quantization in PCM”, IEEE Transactions on Information Theory,  28 (2): 129-137. 
     While the K-means clustering is already capable of segmenting the video image into synthesizable and non-synthesizable regions, it may happen that there are some occluded details, i.e. some details which would be classified as synthesizable region while they may still pertain to a non-synthesizable region. Accordingly, the present disclosure provides an approach that makes use of the fact that details not pertaining to the synthesizable region may be detected in samples of the luminance and/or color (chrominance) channels. For example, the detection as described above may be applied only to luminance (e.g. in Y in the YUV color space) or to one of R, G, B components of the RGB color space, or to a (weighted) average of all color components of a color space, or the like. 
     The fitting and the detection of the samples with the distance to the fitted polynomial exceeding a distance threshold is performed at least in luminance component. 
     In the present disclosure, differences between a polynomial fitted to the luminance and/or color channels identify wrongly clustered details. This is illustrated in the graph  1710  of  FIG. 17 .  FIG. 17  shows luminance values normalized to the range 0 to 1 in the samples  1730  of a two-dimensional video image  1720 . The polynomial to be fitted to the luminance values  1730  of the synthesizable region  1715  (denoted also by a thick frame in  FIG. 17 ) is a plane. Plane fitting provides a computationally efficient means for approximating the luminance of the synthesizable region. 
     A block diagram of the clustering steps can be seen in  FIG. 23 .  FIG. 23  corresponds to  FIG. 8  described above. However, according to the present embodiment,  FIG. 23  additionally includes cluster refinement  2310 . 
     Firstly, a textured region is roughly estimated by an initial clustering step. As mentioned above, in one example, K-means clustering is used. However, the present disclosure is not limited thereto and further clustering approaches, such as Mean Shift or Graphcut or any other clustering, may be applied. 
     In an exemplary implementation a feature vector: 
       F=(R, G, B, u, v) 
     for each sample is built, containing the three color values R, G, B and the picture coordinates u and v denoting particular picture samples (pixels). The K-means clustering of all the five-dimensional vectors finds clusters consisting of samples with similar color and in close spatial proximity. 
     The example of  FIG. 23  shows clustering of a soccer video sequence. Soccer videos often include large grass portions and grass may be efficiently coded as a texture. Due to the spatial proximity of the soccer field lines and the players&#39; shadows to the grass area, these are typically marked as synthesizable. Obviously, the lines and shadows should be excluded from the synthesizable region. Accordingly, the present disclosure provides a refinement of an initial clustering. 
     In particular, in the cluster refinement  2310 , a luminance gradient distance metric is employed to detect these small anomalies in the initially clustered region. It is assumed that the textured region is homogeneously lit. That means if all structural information is removed from the region, the luminance changes smoothly. This behavior is modeled by fitting a plane to the luminance values of the samples in the previously marked region, i.e. region clustered initially as synthesizable. The plane (i.e., a two-dimensional polynomial of the first order) to be fit to the synthesizable region is described by: 
         a 0+ a 1 x+a 2 y=L ( x,y ), 
     where x, y are the sample coordinates, a0, a1, and a2 are the polynomial coefficients, and L(x, y) is the corresponding luminance value of the video image at position (x, y). This gives a linear equation system which is solved for the coefficients a0, a1, and a2. 
       FIG. 17  shows a 3D visualization of the luminance channel. As mentioned above, the luminance values which are typically in the interval from [0,255] with a sample bit depth of 8 or in the interval from [0,1023] with a sample bit depth of 10, are scaled to interval [0,1]. The synthesizable region appears to be smooth. 
       FIG. 18  shows a graph  1800  with the luminance values corresponding to those shown in  FIG. 17  and a polynomial  1810  fitted to the luminance of the synthesizable area. Moreover, the wrongly clustered regions marked by thick-lined rectangles  1815 . Here, it is assumed that the wrongly clustered regions correspond to the regions including one or more samples with the differences to the fitted polynomial higher than the first threshold. 
     In particular, in order to detect the wrongly clustered regions, for each sample in the initially clustered region a distance to the fitted surface is calculated. It is noted that the present disclosure is not limited to calculating distance for each and every sample of the clustered region. While calculation for each sample provides most precise results, some implementations may also reduce complexity by only considering some samples in the initially clustered region (for instance use subsampling). 
     An exemplary distance map calculated for the example of  FIGS. 17 and 18  is shown in  FIG. 19 . In other words,  FIG. 19  shows the difference between the fitted plane and the luminance. As can be seen in the difference map, the brighter portions correspond to the non-synthesizable regions. In particular, in the map, lines and shadows can be easily seen and the first threshold is defined to distinguish between correctly and wrongly clustered samples. 
     The first threshold may be set to a value where all lines and shadows are detected. This can be done manually based on experiments with some training video sequences. Alternatively, the setting of the threshold may be done automatically, depending on the percentage of detected pixels (i.e. pixels detected by initial clustering) to all pixels. In the experience of the inventors, the threshold value is robust to changes and may be set to the same value for all video sequences. However, the present disclosure is not limited thereto and the threshold may be set individually based on various parameters such as a histogram of the colors and/or luminance. 
       FIG. 20  illustrates an example of K-mean clustering applied to an original image. The original image is shown on the left hand side. The result of the K-means clustering is shown on the right hand side. In other words, the white region is determined as synthesizable. As can be seen in the figure, the synthesizable region here still includes portions of the players and the playground lines clearly visible in the original image. 
       FIG. 21  shows on the left hand side the result of the K-mean clustering also already shown on the right hand side of  FIG. 20 . On the right hand side of  FIG. 21  is the result of clustering refinement. As can be seen, the players and the playground lines are now clearly visible. However, the detection of the wrongly clustered regions, depending on the setting of the first threshold, may result in multiple small holes in the synthesizable region, which do not belong to either of players or playground lines. 
     Accordingly, in one exemplary implementation, the processing circuitry is further configured to evaluate the location of the detected samples and to add isolated clusters of the detected samples smaller than a second threshold to the refined texture region. Considering spatial resolutions of typically 720p, 1080p or 4K in broadcasting applications, it is assumed that relevant details (such as the players and the lines) have to have a certain size. Thus, holes smaller than, for instance, 64 samples (not necessarily a squared 8×8 block) are closed. However, the particular second threshold is not limited to 64 samples which are merely exemplary. 
     The second threshold may be, e.g., a number of pixels or samples (possibly in one direction such as horizontal or vertical direction denoting the width or height of a cluster, or the total number of pixels in the cluster). The second threshold may depend on the pixel resolution of the video images analyzed, and may be set either manually, or derived based on an assignment (table, functional relation) between the resolution and the threshold. However, the present disclosure is not limited to any particular threshold setting. The threshold may also be set automatically while considering further parameters such as the variance and/or mean of the texture luminance or any further parameters. 
     The above described further refinement processing corresponds in  FIG. 22  to closing the small black areas in the white synthesizable region. 
     In the present embodiment, the processing circuitry is configured to detect in the identified texture region samples with a distance to the fitted polynomial exceeding a first threshold and to identify a refined texture region as the texture region excluding one or more of the detected samples. It is noted that the refined texture region may exclude all detected samples for which the first threshold is exceeded. These are the samples for which the difference between these image samples and the corresponding respective samples of the fitted polynomial exceed the first threshold. However, as described above, it may be beneficial to only exclude some of the detected samples, i.e. to further refine the clustering by homogenizing the clusters. This may be done by including into the synthesizable region sample clusters of a size smaller than the second threshold. 
     The second threshold may also depend on the first threshold. The lower the first threshold, the more samples will be detected as wrongly classified and the higher may be the second threshold. 
     Moreover, in an exemplary implementation, the processing circuitry is further configured to evaluate the location of the samples of the texture region and to exclude isolated clusters of the texture region from the refined texture region. This corresponds in  FIG. 22  to deleting of the small white clusters within the non-synthesizable regions. As a small cluster, an isolated cluster having a size exceeding a third threshold is determined. The third threshold may have the same size as the second threshold. However, the present disclosure is not limited thereto and the thresholds can differ. The third threshold may be given by a number of pixels or samples. Alternatively, it may be given by a vertical and/or horizontal size of the cluster in number of pixels or samples. However, the thresholds (second and/or third) for detecting the isolated clusters which should be omitted from a non-texture or texture region may be defined in any other way, too, such as proportion of their size to the size of the picture or the like. The term “isolated” means that it is fully surrounded by samples of other cluster(s). 
     The present disclosure is not limited to plane fitting. Rather, a polynomial of a higher order in x and/or y direction may be applied such as quadratic or cubic or the like. 
     In an embodiment, the clustering is discretized into blocks. In general, the blocks may be rectangular with a size M×N or square of a size N×N, with M and N being integers, M&gt;0 and N&gt;1. A block is considered as synthesizable if all samples in this block are synthesizable. Otherwise, the block is not considered as synthesizable (i.e. considered as non-synthesizable). 
     The processing circuitry is further configured to: divide the video image into blocks; determine for each block whether or not it is synthesizable, wherein a block is determined to be synthesizable if all samples in the block belong to the refined texture region and non-synthesizable otherwise; and encode as the texture location information a bitmap which indicates for each block whether or not it is synthesizable according to the determination. 
     The discretization of the image into the blocks enables to efficiently encode the information specifying which parts of the video image belong to the synthesizable region and which parts of the image belong to the non-synthesizable region. It is noted that the bitmap may be further encoded using a variable length code such as a run-length code or another entropy code. 
     The blocks of the video image may have the same size. However, it may be beneficial (as also described above with reference to the prior art), to subdivide the video image to blocks of different shapes and/or sizes. This may enable a more precise distinction between synthesizable and non-synthesizable regions. The hierarchic splitting may be used based on a quad-tree or a binary tree or a mixture thereof to obtain the blocks. 
     Similarly to the encoder, as mentioned above, a decoder may be provided in which the synthesis of the synthesizable image region(s) from one or more patches and additional information is performed. In order to recover at the decoder, which of the image portions are synthesizable and which of the image portions are non-synthesizable, the texture location information may be received (i.e. decoded from the bitstream). As described above, the texture location information may be a block bitmap indicating for each block of the video image whether or not it is synthesizable. For example, the synthesizable regions are identified by decoding a binary symbol map which indicates whether each M×N (or N×N) block is to be synthesized or decoded in another way. This is illustrated in  FIG. 16  by the functional block “Binary mask”. 
     In general, an apparatus is provided for decoding a video image encoded as discussed above, the apparatus comprising a processing circuitry, which is configured to decode the refined texture region separately from portions of the video image not belonging to the refined texture region. 
     The processing circuitry may be further configured to decode a texture location information indicating for each block of a video image whether or not the block belong to the synthesizable portion including texture region. 
     Region Tracking 
     In a video, the size and the location of the texture regions may vary from image to image. On the other hand, in natural video sequences as well as animations and computer graphics, adjacent images (video frames) are typically similar. This is caused by typically smooth movement of the objects and/or background within the image in absence of a scene cut. Correspondingly, it may not be necessary to perform the classification in each and every video image. Instead, region tracking may be performed in order to detect the changes in size and location of the texture region. 
     It is noted that in general, there may be more than one regions with different respective textures and the remaining non-texture part of the frame. In such case, each of the texture regions may be processed as described by the present disclosure, i.e. determined by clustering, represented by a patch and parameters for adjusting the patch. 
     Tracking a region of similar texture through an image stream is important to achieve temporal consistency. To track a region, a tracking algorithm based on feature vectors for the regions is applied according to an exemplary embodiment. If there are prior frames to the newly detected regions of the current frame, these are matched to the previous regions. This can be done by one of several matching algorithms and different feature vectors. The following describes one possible implementation. In this instance it is done by a Hungarian matching algorithm described in H. W Kuhn and B. Yaw, “ The hungarian method for the assignment problem,” Naval Res. Logist. Quart , pp. 83-97, 1955. The Hungarian graphs edge weights are linear combinations of three distances F1, F2, and F3 between regions in the consecutive frames. These are:
         F1: Coordinates of centroid,   F2: Number of pixels, and   F3 Position of pixels.       

     Features based on motion, such as average speed of pixels in clusters, are also possible. There may be further alternative or additional features applied for the purpose of cluster matching. With these features, distance metrics for every combination of clusters for the i-th frame are defined as: 
         D 1= F 1 i   −F 1 i−1    
         D 2= F 2 i   −F 2 i−1    
         D 3=overlap( F 3 −F 3 i−1 ), 
     where the function overlap(•) calculates the number of pixels that are in both clusters, i.e. in clusters of both frames I and i−1. 
     Another distance metric may be defined as follows: 
         D 4=thresh( F 2 i ), 
     where 
     
       
         
           
             
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     The distance D4 penalizes regions that have a too small number of pixels defined by the threshold t. The threshold t is a non-zero integer. The term “inf” stands for infinity, and the distance D4 is set to infinity if the region has less than t pixels, which means that the region is no longer considered as a texture cluster. On the other hand, the pixel regions having t or more than t pixels are considered as texture clusters. Alternatively, the function thresh(F2i) may return t instead of 0 if F2 i ≥&gt;t. 
     The metrics D1 and D3 may consider the motion of a cluster. By summing over the weighted distances a joint distance D may be calculated by: 
     
       
         
           
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     where αi is the weight assigned to the respective distance Di. While the above mentioned features are shape and location based they do not allow detection of quickly changing colors (e.g. a light switching color). Simply adding features and distance metrics based on color similarity enables the algorithm to detect these color changes. If no corresponding region is found the synthesizable region has to be newly determined. 
     The above-described region tracking is a part of decomposition  310  described with reference to  FIG. 3 . In order to perform the decomposition  310 , regions have to be detected by clustering as shown in  FIG. 8  for frames in which new regions occur and have to be tracked for frames in which regions similar to newly detected regions occur. The results of the region tracking may be used to update the block map, which may be signaled to the decoder upon change or regularly. For instance, a flag may be employed indicating whether or not the updated block map is transmitted within a signaling common to or more video pictures. 
     It is noted that the patch extraction  820  is only necessary when the new region is detected. The same patch may be used for the same region tracked over multiple frames. Accordingly, the patch information  850  may be signaled only with the new region. This enables to keep the rate low. However, the present disclosure is not limited, the patch may be updated, i.e. sent even if the corresponding region is still tracked. For instance, the patch may be updated regularly. 
     Motion Compensation 
     In case the texture is only synthesized a single time for a sequence of images with a similar texture, it has to be adjusted (for instance moved and deformed) for each frame. Because one textured region typically deforms uniformly, the motion compensation for this region can be calculated for the whole image. Most detected textures lie on a plane in the underlying 3D scene. That means that camera motions such as pan, tilt, and zoom result in linear deformations of the textured area in the camera plane. Other geometries require a higher order polynomial. The present disclosure is not restricted to planar texture. 
     In order to adjust the synthesized texture, the processing circuitry implementing the texture based coding is further configured to: estimate motion for the texture region; generate motion information according to the estimated motion; and code the motion information into the bitstream. At the decoder, similarly, the motion information is extracted and applied to the synthesized texture in order to adjust it. 
     In particular, according to an embodiment, the estimation of motion is performed by calculating optical flow between the texture region in a first video frame and the texture region in a second video frame preceding the first video frame; and the motion information is a set of parameters which is determined by fitting the optical flow to a two-dimensional polynomial of a first order or a second order. Correspondingly, at the decoder, the set of parameters is extracted from the motion information carried in the bitstream. Then a function given by the set of parameters is applied to the synthesized texture region. 
     According to an exemplary embodiment a plane is calculated that approximates these deformations in x- and y-direction, respectively. The plane corresponding to the deformation u in x-direction is described by the first order polynomial: 
         a 0+ a 1 x+a 2 y=u,    
     where x and y are the image coordinates, and coefficients a0 to a2 are the plane parameters for adjustment in x-direction. Thus can be also written in a vector form as: 
     
       
         
           
             
               
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     With v as deformation in y-direction, the other plane can be defined similarly as 
         a 3+ a 4 x+a 5 y=v.    
     Similarly, x and y are the image coordinates, and coefficients a3 to a5 are the plane parameters for adjustment in y-direction. 
     Accordingly, only six parameters are sufficient in this embodiment to reconstruct the deformation of the synthesized area in two consecutive frames. These polynomial parameters may be signaled through PPS, VPS, and/or slice header. If textures lie on other geometries than planes or a perspective camera model is considered, a higher order polynomial can be used and the corresponding parameters signaled. 
     The deformation is obtained by the dense optical flow between these two frames. In other words, the plane functions u and v are fitted to the optical flow obtained between the two frames. Such dense optical flow algorithm could be the algorithm of D. Sun, S. Roth, and M. J. Black, “ Secrets of optical flow estimation and their principles,” in IEEE Conf. on Computer Vision and Pattern Recognition  ( CVPR ).  IEEE , June 2010, pp. 2432-2439 in the  Classic+NL  implementation known from D. Sun, S. Roth, and M. J. Black.  A quantitative analysis of current practices in optical flow estimation and the principles behind them. IJCV,  106(2), 2014. 
     In this embodiment the optical flow objective function is written in its spatially discrete form as: 
     
       
         
           
             
               
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     where u and v are the horizontal and vertical components of the optical flow fields that is calculated from input images I 1  and I 2 . ρ D  is a data penalty function and ρ S  is a spatial penalty function. λ is a regularization parameter. In other words; the calculation of the optical flow is performed by a function which penalizes higher distances between the components of the optical flow and/or higher differences between the corresponding samples of the first and the second video frame. 
     Here, the quadratic penalty ρ(x)=x 2  is used is used for ρ D  and ρ S , respectively. The objective function is solved using a multi-resolution technique to be able to estimate flow fields with larger displacements. In other words, the optical flow objective function E(u, v) is minimized to find the parameters u and v. 
       FIG. 9  shows the pipeline for the motion compensation technique. The optical flow algorithm calculates the optical flow vector field that is visualized in  FIG. 9 . In particular, optical flow is calculated between current image  901  and previous image  902  as described above. Examples of the calculated optical flow in uv direction and in u direction  910  as shown in  FIG. 9  for the images  901   902 . The calculated optical flow is provided for polynomial fitting  920 . Since the polynomial fitting is only performed for the synthesizable region, i.e. for the texture cluster, the cluster information  930  (corresponding to the cluster information  830 ) is also provided to the polynomial fitting block  920 . The polynomial fitting block  920  calculates polynomial parameters  940 , for instance, corresponding to a0 to a5 mentioned above, by fitting the planes in x and y direction to the calculated optical flow given by u and v. The bottom image in block  910  shows the flow in one image direction (u-direction). It can be seen that in this direction a plane can be fitted to the data as described above. Since motion compensation should only be done in the synthesizable region only, data lying in the region is considered by using the cluster information from the previous step. 
     Correspondingly, at the decoder, parameters a0 to a5 are extracted (parsed) from the bitstream. Samples of the synthesized texture given by coordinates x and y are then processed by calculating the adjusted samples on coordinates u and v of the modified texture region. The term “synthesized texture region” refers to the texture region that is synthesized by filling its samples (pixels) by copying therein the patch. The synthesized texture region may also correspond to the texture region filled with the patch samples an already further processed by adjusting luminance and/or frequency as will be described in the following in more detail. The herein presented adjustments (motion, luminance, frequency) may be applied sequentially in an arbitrary order. Alternatively, only a subset of the adjustment or only one of the adjustments is applied. 
     The above exemplified calculation of the optical flow is merely exemplary and, in general, any other approach for determining optical flow, or in general motion, can be applied. The determined optical flow is fitted to a parametric function in order to describe it by means of a limited set of parameters which may be conveyed to the decoder in order to adjust the texture synthesized from the patch accordingly. Moreover, it is noted that the motion adjustment does not have to be performed by determining optical flow for respective sample positions as shown above. Alternatively, motion vector determination similar to the motion vector determination described above for HEVC based on block matching or template matching may be performed, for instance for small texture blocks such as 2×2 or 4×4 or larger (possibly depending on the image resolution). 
     Luminance Coding 
     Because the luminance of the synthesized area can only contain luminance information included in the patch, only parts of the luminance information of the original image can be conveyed by indicating the patch. 
     Most textured regions are homogeneously lit meaning that there exists a lighting gradient over the textured area. When reconstructing the scene, lighting may be advantageous to obtain an area that blends in visually pleasantly to the neighboring blocks. In one embodiment, the luminance is adapted by extracting the luminance information from the original image a higher order polynomial is fit to the luminance map of the pixels of the whole image synthesizable region. The order of the polynomial depends on the lighting in the scene and determines the number of variables necessary to encode the luminance efficiently. Here, the term “luminance” refers to illumination, i.e. to brightness of the region. This, on the other hand, corresponds to the mean value of the samples in the region. It is noted that the illumination (or luminance) may be calculated only for one color component such as the luminance/luma (Y) or a green component of RGB space. However, it may also be calculated separately for the different color space components. 
     According to an embodiment, the processing circuitry for performing texture encoding is further configured to: determine a set of parameters specifying luminance within the texture region by fitting the texture region samples to a two-dimensional function defined by the set of parameters; and code the set of parameters into the bitstream. 
     The function may be a two-dimensional polynomial. However, it is noted that the present disclosure is not limited thereto and other functions may be applied as well. Nevertheless, polynomial fitting provides an advantage of adjusting the luminance with a relatively small number of parameters to different luminance characteristics. For example, the two-dimensional polynomial has order one or two in each of the two dimensions. 
     Correspondingly, at the decoder, the synthesized texture region is adjusted. At first, the parameters are extracted from the bitstream and the corresponding function is applied to the texture region samples to adjust their luminance. In other words, the processing circuitry at the decoder may be configured to: decode a set of parameters from the bitstream; and the reconstruction further includes calculating a function of the texture patch luminance and the function being defined by the parameters of the set. 
     It is noted that, even though the above-shown embodiments show only one textured and one remaining regions, in general, an image may include one or more different textured regions. In such case, the above embodiments apply to each of the synthesizable regions. 
     A first order polynomial describing the luminance L may be sufficient for most cases: 
         b 0+ b 1 x+b 2 y=L,    
     where x and y are the image coordinates and b0 to b2 are the polynomial parameters. A higher order polynomial can be implemented similarly, by adding further quadratic and/or cubic and/or higher order terms. When there is a visible lighting spot in the area, a second order or higher order polynomial may provide better results. These polynomial parameters may be signaled through PPS, SPS or video parameter set (VPS), or slice header as they fit an entire frame. 
     A visualization of the above procedure can be seen in  FIG. 10 . Input to the luminance fitting is an original image (input image)  1001  and cluster information  1030 , which corresponds to the cluster information  930  and  830  referred to above it reference to  FIGS. 9 and 8 . The cluster information is used to distinguish the texture portions because only the texture portions are used for fitting the luminance. This is illustrated in a three-dimensional plot  1010  of the luminance corresponding to the input image  1001 . After performing the polynomial fitting (first order in this example), the three-dimensional plot  1020  shows the luminance corresponding to plot  1010  together with the fitted plane. The parameters of the fitted plane  1040  are provided as an output for signaling to the decoder, for instance within the bitstream. 
       FIG. 12  shows on the top the enlarged version of the graph  1010 , which shows the luminance of the input image  1001 . On the bottom of  FIG. 12 , the plot  1020  showing the same luminance  1210 , together with the fitted plane  1220  are shown. 
     The term luminance here refers, for instance, to the luma component Y of the YUV color space, i.e. to the samples of one component of a color space. However, the luminance adjustment may be also performed for each of the color components separately based on the same set of parameters or even based on separate sets of parameters determined for each of the color components individually. In case of grayscale images the luminance corresponds to the sample value (brightness). 
     In order to enable a more accurate adjustment of the luminance, a higher order polynomial may be applied. For example, a second order polynomial may be used: 
       ( b   1   ,b   2   ,b   3   ,b   4   ,b   5   ,b   6 )( x,y,xy,x   2   ,y   2 ,1) T =0 
     As described above a higher order polynomial may be fitted to the luminance values in synthesizable area using the cluster information of the clustering step. 
     Correspondingly, at the decoder, parameters b0 to b2 (for 1 st  order polynomial) or parameters b1 to b6 (for 2 nd  order polynomial) are extracted from the bitstream and the corresponding polynomial is applied to all sample positions given by coordinates x and y to obtain adjusted region. It is noted that the parameters bi may be further encoded before inserting them into the bitstream, for instance by a variable length code and/or differential coding. This may be also the case for other parameter sets mentioned in the present disclosure. 
     It is noted that the luminance coding parameters may also be used to define the polynomial used to fit the luminance for the purpose of the clustering refinement described above. This may simplify implementation since the polynomial fitting is only calculated once. In other word, the plane or a polynomial of a higher order defined by parameters b0, b1, . . . may be used to fit the luminance and calculate the differences between the fit and the luminance to detect anomalies and refine clustering. 
     Frequency Adjustment 
     Although a textured region has a similar structure in the whole region it may also include some perspective effects and motion blur. 
     In order to improve the synthesis, according to an embodiment, these effects may be compensated by applying a frequency adjustment. 
     In particular, the processing circuitry implementing encoder based on texture analysis in operation is configured to: identify a texture region within a video frame (picture) and a texture patch for the region, the region including a plurality of image (picture) samples; determine a first set of parameters specifying weighting factors for reconstructing spectral coefficients of the texture region by fitting the texture region in spectral domain to a first function of the texture patch determined according to the first set of parameters; and code the texture patch and the first set of parameters into a bitstream. 
     The identification of the texture region may be performed as discussed above with reference to texture region detection and tracking. The determination of the first function according to the first set of parameters is performed, for instance, in that the first function is a parametric function and the parameters or a subset of parameters of the first function are included or indicated in any way in the first set of parameters. 
     The processing circuitry may be further configured to: transform blocks of the texture region into spectral domain and transform the texture patch into the spectral domain to find for the transformed blocks respective frequency damping parameters by approximating each block with the patch damped with the respective damping parameter. 
     In other words, having the patch block and a fitting function F (the first function) described by fitting function parameters P, for a current block, such fitting function parameters are found, which minimize a cost function between the patch block and the current block in the spectral domain. The cost function may be a minimum mean square error (MMS) or a sum of absolute differences (SAD) or any other function. In particular, the following cost is minimized to obtain the parameter set P: 
       cost=cost_function(current block, F (patch block, P )). 
     The parameter set P is then signaled to the decoder for each block. In order to reduce the number of parameters to be signaled for each block, advantageously, the fitting function F has only one parameter. However, in general the present disclosure is not limited to any particular number of parameters. The parameters P are referred to herein as damping parameters. 
     Alternatively, or in addition, in order to more efficiently convey parameter sets P for each block of the texture region, the step of coding the first set of parameters for all blocks of the texture region may include fitting the damping parameters P determined for the respective blocks and forming a damping parameter map to a damping parameter function DF (also referred to herein as block map function) which is parametrized with one or more parameters DP: 
       damping parameter map= DF ( DP ). 
     Accordingly, in order to signal all damping parameters of all blocks in the texture region, only parameters DP are signaled in the bitstream in this embodiment as the first set of parameters. 
     According to an exemplary implementation, the damping parameters P correspond to one scalar value. However, the present disclosure is not limited to such embodiment. In general, N damping maps may also be constructed for the respective N parameters pi from the set P, i being greater than one and smaller or equal to N which is an integer greater than one. 
     According to an exemplary implementation, the first function DF is a first two-dimensional polynomial. However, the present disclosure is not limited to such implementation and, in general, a different function may be used. The two-dimensional polynomial has order one or two in each of the two dimensions, the two dimensions being vertical and horizontal (covering dumping parameter map in which there is for each block in a two-dimensional image of texture region a value of a damping parameter assigned). However, it is noted that the present disclosure is not limited to any particular degree (order) of the polynomial. In general, any fixed (e.g. predefined in standard) order of polynomial may be used. Alternatively, the order of the polynomial may be also variable and indicated in the bitstream as a part of the first parameter set or elsewhere. 
     For example, the transformation for transforming the texture region into spectral domain is a block-wise discrete cosine transformation. On the other hand, the present disclosure is not limited to such examples and the transformation may be an FFT, DFT, KLT Hadamard or any other linear transformation. 
     In the following, a detailed exemplary embodiment is presented. A block-wise transformation of a block in the original image and a block of the patch is calculated. To fit the block of the patch to the original we introduce a damping function for the AC components of the DCT-coefficients. A new coefficient    
     at position {i,j} in the block is calculated from the coefficient c i,j  of the synthesized region by: 
         =c ij d i+j    
     where d is considered as the constant damping factor for this block. 
     Calculating the damping factor results in a damping map over the textured region. A second order polynomial is fitted to the damping map. Although the damping coefficient is calculated block-wise the fitted polynomial is calculated for the whole image. Since blurring an already high frequency block is visually more pleasing than sharpening it the patch is selected from a highest frequency region. The damping function is not restricted to the above. It can be any function that serves the same purpose. The order of the polynomial can be adapted to the sequence. These polynomial parameters may be signaled through PPS, VPS, and slice header. 
       FIG. 11  shows the pipeline of the frequency adjustment method. In particular, original image (input image)  1101  and the cluster information  1130  (corresponding to the cluster information  1030 ,  930 , and  830  referred to above) are input to block-wise DCT transformation  1120 . The cluster information is used to determine which parts of the image belong to the textures region and thus, which parts of the image are to be processed by the frequency adjustment. In other words, the cluster information indicates which image blocks are to be DCT-transformed. The patch  1150  is also transformed by DCT transformation. The results of both transformations are represented in graphs  1170  and  1160 . In particular, graph  1170  shows DCT coefficient values for a single block. On the other hand, graph  1160  shows the coefficient values calculated for the patch  1150 . By comparing the coefficients of the patch with the coefficients of each of the blocks a damping coefficient for each block can be calculated  1180 . The damping coefficient map  1190  resulting from such calculation is visualized in  FIG. 11  for the input image  1101 . The damping coefficient map is then approximated by a polynomial function and the corresponding polynomial parameters  1140  are signaled. 
     The calculation of the damping coefficient is further visualized in  FIG. 13 . In particular, a block-wise DCT-transform  1120  is calculated in the blocks of the synthesizable regions using the cluster information  830  ( 1130 ) from the previous clustering step  810  in order to determine the blocks belonging to the synthesizable regions. A result for one of the blocks is shown in  FIG. 13  in the graph in the top right corner. In the graph, only AC coefficients, but not the DC coefficient (set to zero), are illustrated. The DCT-transform is also calculated on the extracted patch  850  ( 1150 ) from the patch extraction step  820 . 
     The result of this DCT transformation is illustrated in  FIG. 13  in the top left corner, in which the graph shows AC DCT coefficients  1310  of the patch but not the DC coefficient which is set to zero. 
     Between these DCT-transforms a damping coefficient is calculated for the damping function as explained above. The bottom left corner of  FIG. 13  shows the resulting function  1320  parametrized with the damping coefficient calculated for the block of which the coefficients are listed in the top right corner of  FIG. 13 . The damping function  1320  is to be applied at the decoder to the patch in spectral domain  1310  in coefficient-wise manner to obtain an approximation of the coefficients  1330  of the original block, i.e. the damped coefficients of the patch. The resulting damped coefficients  1340  are illustrated in  FIG. 13  in the bottom right corner. As can be seen in  FIG. 13 , the damped coefficients  1340  are much closer to the coefficients of the original block  1330  than the patch coefficients  1310 . 
     As also described above, the damping function for one block has advantageously only a single parameter d. The parameter d for all blocks of the textured region gives the damping parameter map  1190 . Fitting a polynomial on the parameters in the damping parameter map gives polynomial parameters which may be then signaled. The fitting may be performed similarly as for the luminance adjustment. i.e. with a linear or quadratic, or higher polynomials. 
     Correspondingly to the encoder operation, a decoder is provided for decoding a video frame employing a texture synthesis, the apparatus comprising a processing circuitry configured to: decode from the bitstream a texture patch and a first set of parameters; reconstruct a texture region within a video frame from the texture patch, the region including a plurality of image samples, the reconstruction including weighting spectral coefficients of the patch with a function defined by the first set of parameters. 
     In particular, according to an embodiment the processing circuitry at the decoder is configured to determine damping parameters (P) for the respective blocks of the texture region according to the first function (DF) defined by the first set of parameters (DP); reconstruct the blocks including applying the respective damping parameters (P) to the texture patch in spectral domain; and transform the reconstructed blocks from the spectral domain to spatial domain. 
     Block Artifact Avoidance 
     If a border between synthesized and non-synthesized blocks is still visible this border may be advantageously camouflaged by applying a mincut-algorithm. Several mincut-algorithms are applicable. For example, by overlapping a non-synthesized block with a synthesized block, the Euclidean distance of the luminance values is calculated. A shortest path through the distance matrix is found, for instance, by applying the Dijkstra-algorithm published in  Edsger W Dijkstra: A note on two problems in connexion with graphs. In: Numerische Mathematik.  1, 1959, S. 269-271. 
     The processing circuitry implementing the texture decoding (reconstruction) may apply suppression of block artifacts between blocks of the texture region and blocks of the remaining region, the suppression being performed by:
         (i) calculating a distance matrix between luminance values of overlapped block of the texture region and block of the remaining region,   (ii) calculating the shortest path in the distance matrix,   (iii) combining the block of the texture region and the block of the remaining region along to the calculated shortest path.       

     Alternatively, different deblocking techniques may be applied, such as deblocking filtering used in HEVC. 
     Signaling 
       FIG. 14  illustrates an example of the control information generated at the encoder, which is to be transmitted to the decoder in order to enable correct decoding. In particular, the input image (video frame) is divided into texture region(s) and the remaining image. The remaining image is conventionally coded and the corresponding bitstream provided to the decoder. Each texture region (one or more) is then described by a patch, a block map and a set of adjustment parameters. In particular, a patch representing the text region is extracted from the text region and signaled in a bitstream to the decoder. Blocks or regions (or, in general, samples) pertaining to the texture region are identified and signaled to the decoder. The set of adjustment parameters may include one or more of the following: motion adjustment parameters, luminance adjustment parameters and frequency adjustment parameters. They may be signaled in the respective slice headers or picture parameter sets. 
       FIG. 15  illustrates the corresponding decoder operation. The remaining image portion  1510  is decoded conventionally and combined  1580  with a reconstructed  1570  texture on the blackened portions  1520  of the conventionally decoded image  1510 . The texture is synthesized  1560  by copying the patch  1550  to the image covering at least the texture region  1520  and by modifying  1570  the synthesized texture by at least one of motion, luminance and frequency adjustment according to the respective parametric functions given by the signaled motion  1532 , luminance  1534  and frequency adjustment  1536  parameter sets  1530 . 
     The coefficients of the polynomials for motion, luminance and frequency are floating-point variables in the above described exemplary implementation. The number of variables depends on the chosen representation. Considering that the low number of parameters per slice is not a heavy burden in terms of bit rate overhead, the floating-point value v with an integer value N and a M-bit shift (&gt;&gt;) to the right is straight-forwardly approximated: 
     
       
         
           
             
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     Another approximation is also conceivable. Those parameters can be signaled in the PPS, or VPS, or slice header, or as SEI message, etc. The patch can be signaled as still image in a separate bitstream or in the same bitstream. It is noted that the term bitstream in this disclosure may mean one single bitstream or several parallel bitstreams. The present disclosure is not limited by any particular syntax and semantics of the signaled information of its packetization. 
     In one implementation the synthesizable regions can be signaled as a separate mode for the current block. Another implementation replaces all pixels in the textured regions by a constant color while the synthesizable regions are signaled using a binary map. If multiple synthesizable regions are detected these can also be included in the map. This map can be compressed by common data compression algorithms, for instance gzip or entropy coding based methods. 
     Reconstruction in the Decoder 
     The decoder performs a patch-based texture synthesis, for instance, as known as Image Quilting from A. A. Efros and W. T. Freeman, “Image quilting for texture synthesis and transfer,” in Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, ser. SIGGRAPH &#39;01, New York, N.Y., USA: ACM, 2001, pp. 341-346 or GraphCut Textures known from V. Kwatra, A. Schödl, I. Essa, G. Turk, and A. Bobick, “Graphcut textures: Image and video synthesis using graph cuts,” ACM Transactions on Graphics, SIGGRAPH 2003, vol. 22, no. 3, pp. 277-286, July 2003. Other texture synthesis algorithms are conceivable. The synthesis algorithm returns an image slightly larger than the region that is reconstructed that is simply pasted to the blocks in synthesis-mode. This only needs to be done once for a sequence of images containing the same textured region. This ensures temporal coherence as well as keeping computational effort low. The surfaces corresponding to motion vector fields are reconstructed. The texture image is transformed according to them. Because the texture image consists of subsamples of the patch its luminance is homogeneous. Therefore, the mean luminance of the patch is subtracted from the reconstructed luminance surface. This mean luminance difference is added to the reconstructed area. Reconstruction of the frequency is by applying the reconstructed damping factor block-wise. 
       FIG. 16  shows an exemplary decoder pipeline described above. Using the received information on the left side of the figure the decoder performs the texture synthesis of the large texture image and sequentially performs the motion compensation, luminance reconstruction and frequency reconstruction to the large textured area. This large region is copied in the decoded picture at the regions given by the binary map. As described above, the binary map may be on a sample basis or on a block basis. 
     In summary, according to an embodiment, the present disclosure relates to an apparatus for encoding a video signal, wherein the video signal comprises a plurality of frames, each frame is dividable into a plurality of blocks, each block comprises a plurality of pixels, and each pixel is associated with at least one pixel value (also referred to as sample value). The encoding apparatus comprises a prediction module for intra prediction configured to generate a prediction block for a current, i.e. currently processed block on the basis of a reconstructed area comprising at least one already generated reconstructed block adjacent to the current block, wherein the prediction module is configured to implement the disclosed texture synthesis algorithm. A reconstructed block is a block reconstructed from a predicted block and an error block. 
     In one possible implementation the apparatus is configured to provide the encoded video signal in the form of a bit stream, wherein the bit stream comprises information about the disclosed signaling method for motion, luminance and frequency damping. 
     In other words, the encoder may perform the following processing:
         Detect synthesizable regions in a picture and track them through a picture stream,   Encode non-synthesizable regions in a conventional way,   The synthesizable region is coded as an additional coding mode, or as an integer map coded by conventional encoders while replacing color information in the synthesizable blocks with a constant color,   Extract one or more image patches from synthesizable regions,   Extract motion information by employing the disclosed hyper plane fitting,   Extract luminance information by employing the disclosed polynomial fitting,   Extract frequency information by employing the disclosed polynomial fitting.       

     One or more of the above steps may be performed. 
     In other words, when simply replacing the synthesizable area with the patch, important information is lost. Based on the above provided embodiments, exemplary combined implementation has been tested applying:
         Motion adjustment (x,y domain) using 1st order polynomial or higher (same for whole frame),   Luminance adjustment (x,y domain) using 1st order polynomial or higher (same for whole frame),   Frequency adjustment (frequency/DCT domain) using 1st/2nd order or higher (on block-level).       

     By fitting higher order polynomials to the information in the synthesizable area only a minor number of variables are sufficient to plausibly reconstruct this information. Experiments have shown that as few as 15 variables are sufficient. These variables can be signaled for example in the slice header. 
     According another embodiment, the present disclosure relates to a corresponding apparatus for decoding an encoded bit stream based on a video signal, wherein the video signal comprises a plurality of frames, each frame is dividable into a plurality of blocks, each block comprises a plurality of pixels, and each pixel is associated with at least one pixel value. The decoding apparatus comprises a prediction module for intra prediction configured to generate a prediction block for a current, i.e. currently processed block on the basis of a reconstructed area comprising at least one already generated reconstructed block adjacent to the current block, wherein the prediction module is configured to implement the disclosed texture synthesis algorithm. 
     In other words, the decoder may perform the following processing:
         Decode non-synthesizable regions in a conventional way,   Reconstruct a region from one or multiple image patches,   Deform the reconstructed region to achieve visually plausible motion using hyper plane reconstruction from additionally signaled variables,   Reconstruct luminance gradients using polynomial reconstruction from additionally signaled variables,   Reconstruct frequencies in the textured region using polynomial reconstruction from additionally signaled variables,   Deblock to avoid visible borders between synthesized and non-synthesized regions.       

     One or more of the above steps may be performed. 
     The present disclosure may be implemented in an apparatus and/or processing circuitry. Such apparatus may be a combination of a software and hardware or may be implemented only in hardware or only in software to be run on a computer or any kind of processing circuitry. For example, the texture synthesis and analysis may be implemented in a separate processing circuitry or in the same processing circuitry as the remaining texture encoding and decoding processing. The processing circuitry may be an integrated circuit. Moreover, the texture region encoding and decoding may be implemented in the same or different processing circuitry as the conventional video coder and decoder used to code and decode the remaining non-synthesizable image regions. 
     The processing described in the present disclosure may be performed by any processing circuitry, such as one or more chip (integrated circuit), which may be a general purpose processor, or a digital signal processor (DSP), or a field programmable gate array (FPGA), or the like. However, the present disclosure is not limited to implementation on a programmable hardware. It may be implemented on an application-specific integrated circuit (ASIC) or by a combination of the above mentioned hardware components. 
     According to an aspect, an apparatus is provided for encoding a video picture employing a texture synthesis. The apparatus comprises a processing circuitry which is configured to: identify a texture region within a video picture and a texture patch for the region, the region including a plurality of picture samples; determine a first set of parameters specifying weighting factors for reconstructing spectral coefficients of the texture region by fitting the texture region in a spectral domain to a first function of the texture patch, the first function being determined according to the first set of parameters; and code the texture patch and the first set of parameters into a bitstream. 
     Such frequency damping provides the possibility of adapting the reconstructed one or more blocks (region) to the original, by adjusting the spectrum of the patch. 
     In one embodiment, the processing circuitry is configured to: transform one or more blocks of the texture region into the spectral domain; transform the texture patch into the spectral domain; find for the transformed one or more blocks respective frequency damping parameters by approximating each transformed block with the transformed texture patch damped with the respective damping parameter; and coding the first set of parameters by fitting the frequency damping parameters determined for the respective blocks to a block map function. 
     Such coding may substantially reduce the additional overhead caused by the signaling of the first parameter set as the damping parameters may vary per block, but only parameters of the function approximating the damping parameters of all blocks are to be transmitted. 
     For example, the damping parameter is a scalar value. This further reduces the overhead. 
     The first function is, for instance, a first two-dimensional polynomial. A two dimensional polynomial provides a very efficient way of signaling with a sufficient variability to adapt the damping parameters. The order of the polynomial may be also signaled. In one example, the first two-dimensional polynomial has order one or two in each of the two dimensions, the two dimensions being vertical and horizontal. 
     The transformation for transforming the texture region into spectral domain may be a block-wise discrete cosine transformation. 
     In one embodiment, combinable with any of the above embodiments and examples, the processing circuitry is further configured to: determine a second set of parameters specifying luminance within the texture region by fitting the texture region samples to a second two-dimensional function defined by the second set of parameters, and code the second set of parameters into the bitstream. Additional adjustment of illumination enables more precise synthesis of the texture regions corresponding to typical illumination situation of real video sequences. 
     For example, the second function is a two-dimensional polynomial. The two-dimensional polynomial may have order one or two in each of the two dimensions. 
     The processing circuitry is further configured to: estimate motion for the texture region; generate motion information according to the estimated motion; and code the motion information into the bitstream. Adaption to motion is another mean for making the synthesized regions closer to the captured video images which typically include smooth motions well modelable by means of a set of parameters. 
     According to an embodiment, the estimation of motion is performed by calculating an optical flow between the texture region in a first video picture and the texture region in a second video picture preceding the first video picture. The motion information is a third set of parameters which is determined by fitting the optical flow to a two-dimensional polynomial of a first order or a second order. 
     The calculation of the optical flow may be performed by a function that penalizes higher distances between the components of the optical flow and/or higher differences between the corresponding samples of the first and the second video picture. This is to reflect the typical optical flow characteristics in natural video sequences. 
     According to another embodiment, combinable with any of the above embodiments, the processing circuitry is further configured to apply a suppression of block artifacts between blocks of the texture region and blocks of a remaining region, the suppression being performed by: (i) calculating a distance matrix between luminance values of an overlapped block of the texture region and a block of the remaining region, (ii) calculating the shortest path in the distance matrix, and (iii) combining the block of the texture region and the block of the remaining region along the calculated shortest path. 
     The processing circuitry may be further configured to: detect the texture region within a video picture by using clustering, generate texture region information indicating the location of the texture region within the video picture, and insert the texture region information into the bitstream. 
     According to an aspect, an apparatus is provided for decoding a video picture employing a texture synthesis, the apparatus comprising a processing circuitry configured to: decode from the bitstream a texture patch and a first set of parameters; reconstruct a texture region within a video picture from the texture patch, the region including a plurality of picture samples, the reconstruction including weighting spectral coefficients of the patch with a function determined by the first set of parameters. 
     In one embodiment, the processing circuitry is configured to: determine damping parameters for the respective blocks of the texture region according to a block map function determined according to the first set of parameters; reconstruct the blocks including applying the respective damping parameters to the texture patch in spectral domain; and transform the reconstructed blocks from the spectral domain to spatial domain. 
     The damping parameter may be a scalar value. The first function may be first two-dimensional polynomial. For example, the first two-dimensional polynomial has order one or two in each of the two dimensions, the two dimensions being vertical and horizontal. 
     The transformation for transforming the texture region into spectral domain may be a block-wise discrete cosine transformation. 
     According to an embodiment, the processing circuitry is further configured to: decode a second set of parameters from the bitstream; and wherein the reconstruction further includes calculating a function of the texture patch luminance (illumination) and the function being defined by the parameters of the second set. 
     The second function is, for instance, a two-dimensional polynomial. The two-dimensional polynomial may have order one or two in each of the two dimensions. 
     The processing circuitry is further configured to: parse (decode) from the bitstream motion information; based on the motion information determine motion compensation function; apply motion compensation function to the synthesized texture portion. 
     It is noted that the determination of the motion compensation may be performed by means of parametrized function of which the parameters are signaled in the bitstream within the third set of parameters. 
     For example, the determination of the motion compensation function may be, for instance a two-dimensional polynomial of a first order or a second order for approximating an optical flow between the texture region in a first video picture and the texture region in a second video picture preceding the first video picture. 
     The motion information is the third set of parameters and indicates (for instance directly codes) the parameters of the motion compensation function such as polynomial parameters and possibly also order. However, the order may be alternatively predefined, for instance by the standard. It is noted that the motion compensation is then applied to the texture to be synthesized. 
     According to an embodiment, the processing circuitry is further configured to apply a suppression of block artifacts between blocks of the texture region and blocks of a remaining region, the suppression being performed by: (i) calculating a distance matrix between luminance values of an overlapped block of the texture region and a block of the remaining region, (ii) calculating the shortest path in the distance matrix, (iii) combining the block of the texture region and the block of the remaining region along the calculated shortest path. 
     According to an embodiment, the processing circuitry is further configured to: parsing from the bitstream texture region information indicating the location of the texture region within the video picture. The texture region information may be for instance, a bitmap indicating for each block of picture whether it belongs either to synthesizable (texture) region or to the remaining portion of the picture (non-synthesizable region). This information may be further used for determining which parts of the remaining picture region and the synthesized texture are to be combined. Also, the above described reconstruction processing is only necessary for the texture region so that in order to reduce the complexity on the decoder, only these portion may be processed by adjusting the texture by motion compensation and/or luminance compensation and/or frequency damping. 
     According to an aspect, a method is provided for encoding a video picture employing a texture synthesis, the method comprising the steps of: identifying a texture region within a video picture and a texture patch for the region, the region including a plurality of picture samples; determining a first set of parameters specifying weighting factors for reconstructing spectral coefficients of the texture region by fitting the texture region in spectral domain to a first function of the texture patch, the first function being determined by the first set of parameters; and coding the texture patch and the first set of parameters into a bitstream. 
     In one embodiment, the method further comprises transformation of one or more blocks of the texture region into the spectral domain; transformation of the texture patch into the spectral domain; calculation, for the transformed one or more blocks, respective frequency damping parameters by approximating each transformed block with the transformed texture patch damped with the respective damping parameter; and coding the first set of parameters by fitting the frequency damping parameters determined for the respective blocks to a block map function. For example, the damping parameter is a scalar value. 
     The first function is, for instance, a first two-dimensional polynomial. The order of the polynomial may be also signaled in one exemplary embodiment. In one example, the first two-dimensional polynomial has order one or two in each of the two dimensions, the two dimensions being vertical and horizontal. 
     The transformation for transforming the texture region into spectral domain may be a block-wise discrete cosine transformation. 
     In one embodiment, combinable with any of the above embodiments and examples, the method further comprises the steps of: determining a second set of parameters specifying luminance within the texture region by fitting the texture region samples to a second two-dimensional function defined by the second set of parameters, and coding the second set of parameters into the bitstream. 
     For example, the second function is a two-dimensional polynomial. The two-dimensional polynomial may have order one or two in each of the two dimensions. 
     The method may further include the estimating of motion for the texture region; generating motion information according to the estimated motion; and coding the motion information into the bitstream. According to an embodiment, the estimation of motion is performed by calculating an optical flow between the texture region in a first video picture and the texture region in a second video picture preceding the first video picture. The motion information is a third set of parameters which is determined by fitting the optical flow to a two-dimensional polynomial of a first order or a second order. 
     The calculation of the optical flow may be performed by a function which penalizes higher distances between the components of the optical flow and/or higher differences between the corresponding samples of the first and the second video picture. 
     According to another embodiment, combinable with any of the above embodiments, the method further comprises the steps of applying a suppression of block artifacts between blocks of the texture region and blocks of a remaining region, the suppression being performed by: (i) calculating a distance matrix between luminance values of an overlapped block of the texture region and a block of the remaining region, (ii) calculating the shortest path in the distance matrix, and (iii) combining the block of the texture region and the block of the remaining region along the calculated shortest path. 
     The method may further include detecting the texture region within a video picture by using clustering; generating texture region information indicating the location of the texture region within the video picture; and inserting the texture region information into the bitstream. 
     According to another aspect, a method is provided for decoding a video picture employing a texture synthesis, the method comprising the steps of: decoding from the bitstream a texture patch and a first set of parameters; reconstructing a texture region within a video picture from the texture patch, the region including a plurality of picture samples, the reconstruction including weighting spectral coefficients of the patch with a function determined by the first set of parameters. 
     In one embodiment, the method further comprises the steps of determining damping parameters for the respective blocks of the texture region according to a block map function determined according to the first set of parameters; reconstructing the blocks including applying the respective damping parameters to the texture patch in spectral domain; and transforming the reconstructed blocks from the spectral domain to spatial domain. 
     The damping parameter may be a scalar value. The first function may be first two-dimensional polynomial. For example, the first two-dimensional polynomial has order one or two in each of the two dimensions, the two dimensions being vertical and horizontal. 
     The transformation for transforming the texture region into spectral domain may be a block-wise discrete cosine transformation. According to an embodiment, the method further comprises: decoding a second set of parameters from the bitstream; and wherein the reconstruction further includes calculating a function of the texture patch luminance (illumination) and the function being defined by the parameters of the second set. 
     The second function is, for instance, a two-dimensional polynomial (vertical and horizontal direction). The two-dimensional polynomial may have order one or two in each of the two dimensions. 
     The method may further include parsing (decode) from the bitstream motion information; based on the motion information, determining motion compensation function; and applying motion compensation function to the synthesized texture portion. For example, the determination of the motion compensation function may be, for instance a two-dimensional polynomial of a first order or a second order for approximating an optical flow between the texture region in a first video picture and the texture region in a second video picture preceding the first video picture. 
     The motion information is the third set of parameters and indicates (for instance directly codes) the parameters of the motion compensation function such as polynomial parameters and possibly also order. However, the order may be alternatively predefined, for instance by the standard. It is noted that the motion compensation is then applied to the texture to be synthesized. 
     According to an embodiment, the method further includes applying a suppression of block artifacts between blocks of the texture region and blocks of a remaining region, the suppression being performed by: (i) calculating a distance matrix between luminance values of an overlapped block of the texture region and a block of the remaining region, (ii) calculating the shortest path in the distance matrix, (iii) combining the block of the texture region and the block of the remaining region along the calculated shortest path. 
     According to an embodiment, the method also includes parsing from the bitstream texture region information indicating the location of the texture region within the video picture. The texture region information may be for instance, a bitmap indicating for each block of picture whether it belongs either to synthesizable (texture) region or to the remaining portion of the picture (non-synthesizable region). This information may be further used for determining which parts of the remaining picture region and the synthesized texture are to be combined. Also, the above described reconstruction processing is only necessary for the texture region so that in order to reduce the complexity on the decoder, only these portion may be processed by adjusting the texture by motion compensation and/or luminance compensation and/or frequency damping. 
     The present disclosure relates to encoding a decoding video employing texture coding. In particular, a texture region is identified within a video picture and a texture patch is determined for the region. Clustering is performed to identify a texture region within the video image. The clustering is further refined. In particular, one or more brightness parameters of a polynomial is determined by fitting the polynomial to the identified texture region. In the identified texture region, samples are detected with a distance to the fitted polynomial exceeding a first threshold and identify a refined texture region as the texture region excluding one or more of the detected samples. Finally, the refined texture region is encoded separately from portions of the video image not belonging to the refined texture region.