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Timestamp: 2013-06-19 12:05:03
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Low Complexity Transform Coding Using Adaptive Dct/dst For Intra-prediction 11 views for this patent on FreshPatents.comupdated 06/14/13
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Low complexity transform coding using adaptive dct/dst for intra-prediction Abstract: A method and apparatus encode and decode video by determining whether to use discrete cosine transform (DCT) and DST for each of the horizontal and vertical transforms. During encoding, an intra-prediction is performed based on an intra-prediction mode determined for an M×N input image block to obtain an M×N intra-prediction residue matrix (E). Based on the intra-prediction mode, each of a horizontal transform and a vertical transform is performed using one of DCT and DST according to the intra-prediction mode. During decoding, the intra-prediction mode is determined from an incoming video bitstream. The M×N transformed coefficient matrix of the error residue is obtained from the video bitstream using an inverse quantizer. Based on the intra prediction mode, one of DCT and DST is performed for each of an inverse vertical transform and an inverse horizontal transform. ...
Agent: - Suwon-si, KRInventors: Ankur Saxena, Felix Carlos FernandesUSPTO Applicaton #: #20120057630 - Class: 37524003 (USPTO) - 03/08/12 - Class 375 Related Terms: Coding Complexity Decode Discrete Discrete Cosine Transform Encode Inverse Transform The Patent Description & Claims data below is from USPTO Patent Application 20120057630, Low complexity transform coding using adaptive dct/dst for intra-prediction.
The present application is related to U.S. Provisional Patent Application No. 61/380,991, filed Sep. 8, 2010, entitled “LOW COMPLEXITY TRANSFORM CODING USING ADAPTIVE DCT/DST FOR INTRA PREDICTION”, U.S. Provisional Patent Application No. 61/427,758, filed Dec. 28, 2010, entitled “ON OPTIMALITY OF INTRA-PREDICTION MODE MAPPINGS AND LOCATION SPECIFIC CHOICE FOR ADAPTIVE DCT/DST”, U.S. Provisional Patent Application No. 61/444,045, filed Feb. 17, 2011, entitled “LOW COMPLEXITY ALTERNATE TO DST TYPE 7 IN MODE-DEPENDENT DCT/DST FOR INTRA PREDICTION IN VIDEO CODING”, U.S. Provisional Patent Application No. 61/449,484, filed Mar. 4, 2011, entitled “FAST IMPLEMENTATION TO DST TYPE 7 IN MODE-DEPENDENT DCT/DST FOR INTRA PREDICTION IN VIDEO CODING”, U.S. Provisional Patent Application No. 61/473,047, filed Apr. 7, 2011, entitled “FAST IMPLEMENTATION FOR DST TYPE 7” and U.S. Provisional Patent Application No. 61/475,123, filed Apr. 13, 2011, entitled “FAST IMPLEMENTATION FOR FORWARD AND INVERSE DST TYPE 7”. Provisional Patent Applications Nos. 61/380,991, 61/427,758, 61/444,045, 61/449,484, 61/473,047 and 61/475,120 are assigned to the assignee of the present application and is hereby incorporated by reference into the present application as if fully set forth herein. The present application hereby claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Applications Nos. 61/380,991, 61/427,758, 61/444,045, 61/449,484, 61/473,047 and 61/475,120.
The present application relates generally to a video encoder/decoder (codec) and, more specifically, to a method and apparatus for implementing a mode-dependent DCT/DST video codec in which discrete cosine transform (DCT) and discrete sine transform (DST) are selected based on intra-prediction residual energy, or simply intra-prediction modes.
Most existing image and video-coding standards such as JPEG, H.264/AVC, VC-1, and the upcoming next generation video codec standard HEVC (High Efficiency Video Coding) employ block-based transform coding as a tool to efficiently compress the input image and video signals. The pixel domain data is transformed to frequency domain using a transform process on a block-by-block basis. For typical images, most of the energy is concentrated in the low-frequency transform coefficients. Following the transform, a bigger step-size quantizer can be used for higher-frequency transform coefficients in order to compact energy more efficiently and attain better compression. Hence, it is required to devise the optimal transform for each image block to fully de-correlate the transform coefficients. The Karhunen Loeve Transform (KLT) possesses several attractive properties, e.g., in high resolution quantization of Gaussian signals and full de-correlation of transform coefficients. However, practical use of KLT is limited due to its high computational complexity, and it has been shown in “Discrete cosine transform-algorithms, advantages and applications,” by K. R. Rao and P. Yip (1990), that the Discrete Cosine Transform (DCT) provides an attractive alternative to KLT in terms of energy compaction and performance close to KLT. But with the advent of intra-prediction, this is no longer the case and the optimal transform should be adaptive to intra-prediction mode.
In the ongoing standardization of HEVC, non-conventional transforms, in addition to the standard DCT are being investigated for intra-prediction residuals (Robert Cohen et. al., “Tool Experiment 7: MDDT Simplification”, ITU-T JCTVC-B307, Geneva, Switzerland, July 2010). These transforms can broadly be categorized into two classes: (a) training-based transforms and (b) model-based transforms. Prominent amongst the training based transforms is the Mode-Dependent Directional Transforms (MDDT) (Y. Ye and M. Karczewicz, “Improved Intra coding,” ITU-T Q.6/SG-16 VCEG, VCEG-AG11, Shenzhen, China, October 2007). In MDDT, a large training set of error residuals is collected for each intra-prediction mode and then the optimal transform matrix is computed using the residual training set. However, MDDT requires a large number of transform matrices—up to eighteen at block sizes N=4 and 8. The other class of model-based transform assumes the video signal to be modeled a first order Gauss-Markov process and then the optimal transform is derived analytically. These model based transforms require two transform matrices at a block size.
In J. Han, A. Saxena and K. Rose, “Towards jointly optimal spatial prediction and adaptive transform in video/image coding,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar. 2010, pp. 726-729, a Discrete Sine Transform (DST) was analytically derived with frequency and phase components different from the conventional DCT for the first-order Gauss-Markov model, when the boundary information is available in one direction, as in intra-prediction in H.264/AVC (T. Wiegland, G. J. Sullivan, G. Bjontegaard and A. Luthra, “Overview of the H.264/AVC video coding standard,” IEEE Transactions on Circuits and Systems for Video Technology, July 2003). They also showed that if prediction is not performed along a particular direction, then DCT performs close to KLT. The idea was applied to the vertical and horizontal modes in intra-prediction in H.264/AVC and a combination of the proposed DST and conventional DCT was used adaptively. Attempts have been made to extend similar ideas experimentally without a theoretical justification, by applying the combination of DST and DCT to other seven prediction modes in H.264/AVC, and showed that there is only a minor loss in performance in comparison to MDDT (C. Yeo, Y. H. Tan, Z. Li and S. Rahardja, “Mode-dependent fast separable KLT for block-based intra coding,” ITU-T JCTVC-B024, Geneva, Switzerland, July 2010).
Also, the DST matrices should be appropriately scaled to take into account the effect of quantization scaling matrices. The prior art does not describe modification of DST matrix coefficients to match the scaling to the DCT in the implementation in the HEVC.
Therefore, there is a need in the art for an improved video codec that improves the compression efficiency and utilizes a low complexity transform.
According to one aspect of the present disclosure, a method for decoding video is provided. An intra-prediction mode is determined from an incoming video bitstream. Coefficients of the incoming video bitstream are mapped to an M×N transformed coefficient matrix according to an intra-prediction mode of the incoming video bitstream. A first one of discrete cosine transform (DCT) and discrete sine transform (DST) is determined to be applied as an inverse vertical transform, and a second one of DCT and DST is determined to be applied as an inverse horizontal transform for the transformed coefficient matrix according to the intra-prediction mode. An inverse transform comprising the inverse vertical transform and the inverse horizontal transform is is performed, using the first one of DCT and DST for the inverse vertical transform and the second one of DCT and DST for the inverse horizontal transform to calculate an approximation of an error prediction residual to be used for reconstructing image of a video.
According to another aspect of the present disclosure, a method for encoding video is provided. Intra-prediction is performed on an input matrix of an M×N input image block (X) based on an intra-prediction mode to generate a prediction {tilde over (X)} and obtain an M×N intra-prediction residue matrix (E). A first one of discrete cosine transform (DCT) and discrete sine transform (DST) is determined to be applied as a horizontal transform, and a second one of DCT and DST is determined to be applied as a vertical transform for E according to the intra-prediction mode. A forward transform comprising the horizontal transform and the vertical transform is performed, using the first one of DCT and DST as the horizontal transform and the second one of DCT and DST for the vertical transform to calculate a transformed coefficient matrix (E2).
According to another aspect of the present disclosure, an apparatus for decoding video is provided. The apparatus includes an inverse quantizer and an inverse transform unit. The inverse quantizer maps quantized transformed coefficient indices obtained from an incoming video bitstream to an M×N transformed coefficient matrix according to an intra-prediction mode of the incoming video bitstream. Using the M×N transformed coefficient matrix and the intra prediction mode obtained from the incoming video bitstream, the inverse transform unit determines to apply a first one of discrete cosine transform (DCT) and discrete sine transform (DST) as an inverse vertical transform, and a second one of DCT and DST as an inverse horizontal transform for the transformed coefficient matrix according to the intra-prediction mode, and performs and inverse transform comprising the inverse vertical transform and the inverse horizontal transform, using the first one of DCT and DST for the inverse vertical transform and the second one of DCT and DST for the inverse horizontal transform to calculate an approximation of an error prediction residual.
According to yet another aspect of the present disclosure, an apparatus for encoding a video is provided. The apparatus includes a unified intra-prediction unit and a transform unit. The unified intra-prediction unit performs intra-prediction on an input matrix of an M×N input image block (X) based on an intra-prediction mode to generate {tilde over (X)} and obtain an M×N intra-prediction residue matrix (E). The transform unit determines to apply a first one of discrete cosine transform (DCT) and discrete sine transform (DST) as a horizontal transform and a second one of DCT and DST as a vertical transform for E according to the intra-prediction mode, and performs a forward transform comprising the horizontal transform and the vertical transform, using the first one of DCT and DST as the horizontal transform and the second one of DCT and DST for the vertical transform to calculate a transformed coefficient matrix (E2).
FIG. 1A illustrates a high-level block diagram of a video encoder according to an embodiment of the present disclosure;
FIG. 1B illustrates a high-level block diagram of a video decoder according to an embodiment of the present disclosure;
FIG. 2 is a block diagram of a portion of a video encoder according to an embodiment of the present disclosure;
FIGS. 3A and 3B are diagrams illustrating prediction of image pixels using the Category 1 oblique modes, according to an embodiment of the present disclosure;
FIG. 4 is a diagram illustrating prediction of image pixels using the Category 2 oblique modes, according to an embodiment of the present disclosure;
FIG. 5 is a diagram illustrating prediction of image pixels using the DC prediction mode, according to an embodiment of the present disclosure;
FIG. 6 is a diagram illustrating the derivation of transforms for Category 1 oblique modes according to an embodiment of the present disclosure;
FIGS. 7A and 7B are diagrams illustrating the derivation of transforms for Category 2 oblique modes according to an embodiment of the present disclosure;
FIG. 8 illustrates a process for encoding a video bitstream using DCT/DST for intra-prediction according to an embodiment;
FIG. 9 is a block diagram of a portion of a video decoder according to an embodiment of the present disclosure;
FIG. 10 illustrates a process for decoding a video bitstream using DCT/DST according to an embodiment;
FIG. 11 is a diagram illustrating prediction of pixels using Category 2 oblique modes by partitioning the columns into two regions according to an embodiment of the present disclosure; and
FIG. 12 is a diagram illustrating the derivation of transforms for Category 2 oblique modes when the columns are partitioned into two regions according to an embodiment of the present disclosure;
FIG. 13 illustrates charts of basis functions for DST Type-4 and DST Type-7 for 4-point DSTs according to an embodiment of the present disclosure; and
FIG. 14 illustrates charts of basis functions for DST Type-4 and DST Type-7 for 8-point DSTs according to an embodiment of the present disclosure.
FIGS. 1A through 14, discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way to limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged video encoder or decoder.
The present disclosure is directed to selecting between Discrete Sine Transform (DST) and Discrete Cosine Transform (DCT) for various prediction modes in intra-coding for video. Choosing between DST and conventional DCT based on intra-prediction modes optimally yields substantial compression gains. The embodiments of the present disclosure use a low complexity transform and requires only one DST transform matrix, resulting in a fast implementation of DST. Although embodiments of the present disclosure will be described with reference to the HEVC (High Efficiency Video Coding) standard, one of ordinary skill in the art would recognize that the embodiments may also be applicable to the H.264/AVC standard.
The Joint Collaborative Team on Video Coding (JCT-VC) is considering a Test Model Under Consideration (TMuC) (“Test Model under Consideration,” ITU-T JCTVC-B205_draft002, Geneva, Switzerland, July 2010) for standardization of the HEVC video codec.
FIG. 1A illustrates a high-level block diagram of a video encoder according to an embodiment of the present disclosure. Encoder 100 is based on a coding unit, according to an embodiment of the present invention. An intra prediction unit 111 performs intra prediction on prediction units of the intra mode in a current frame 105, and a motion estimator 112 and a motion compensator 115 performs inter prediction and motion compensation on prediction units of the inter-prediction mode using the current frame 105 and a reference frame 145.
Residual values are generated based on the prediction units output from the intra-prediction unit 111, the motion estimator 112, and the motion compensator 115. The generated residual values are output as quantized transform coefficients by passing through a transform unit 120 and a quantizer 122.
The quantized transform coefficients are restored to residual values by passing through an inverse quantizer 130 and an inverse transform unit 132, and the restored residual values are post-processed by passing through a de-blocking unit 135 and a loop filtering unit 140 and output as the reference frame 145. The quantized transform coefficients may be output as a bitstream 127 by passing through an entropy encoder 125.
FIG. 1B is a high-level block diagram of a video decoder based on a coding unit according to an embodiment of the present disclosure. A bitstream 155 passes through a parser 160 so that encoded image data to be decoded and encoding information necessary for decoding are parsed. The encoded image data is output as inverse-quantized data by passing through an entropy decoder 162 and an inverse quantizer 165 and restored to residual values by passing through an inverse transform unit 170. The residual values are restored according to rectangular block coding units by being added to an intra prediction result of an intra prediction unit 172 or a motion compensation result of a motion compensator 175. The restored coding units are used for prediction of next coding units or a next frame by passing through a de-blocking unit 180 and a loop filtering unit 182.
To perform decoding based on a decoding method according to an embodiment of the present invention, components of the image decoder 150, i.e., the parser 160, the entropy decoder 162, the inverse quantizer 165, the inverse transform unit 170, the intra prediction unit 172, the motion compensator 175, the de-blocking unit 180 and the loop filtering unit 182, perform the image decoding process.
Each functional aspect will now be described.
Intra-Prediction (111/172): Intra-prediction utilizes spatial correlation in each frame to reduce the amount of transmission data necessary to represent the picture. Intra-frame is essentially the first frame to encode but with less amount of compression. Additionally there can be some intra blocks in an inter frame. Intra-prediction involves making predictions within a frame whereas inter-prediction involves making predictions between frames. The present disclosure is mainly focused on intra-prediction.
Motion Estimation (112): The fundamental concept in video compression is to store only incremental changes between frames when inter-prediction is performed. The differences between blocks in two frames are extracted by a Motion Estimation tool. Here a predicted block is reduced to a set of motion vectors and inter-prediction residues.
Motion Compensation (115/175): Motion Compensation will decode the image that is encoded by Motion Estimation. This reconstruction of image is done from received motion vectors and the block in the reference frame.
Transform (120/132/170): The transform unit is used to compress the image in inter-frames or intra-frames. The most commonly used transform is Discrete Cosine Transform (DCT).
Quantization (122/130/165): The quantization stage reduces the amount of information by dividing each transform coefficient by a particular number to reduce the quantity of possible values that each transform coefficient value could have. Because this makes the values fall into a narrower range, this allows entropy coding to express the values more compactly.
De-blocking and Loop Filters (135/140/182): The role of de-blocking is to remove the encoding artifacts due to block-by-block coding of an image. The de-blocking filter acts on the boundaries of the image blocks, and removes the blocking artifacts. The role of the loop filter is to minimize the mean-squared error between the original image pixels and reconstructed image pixels. In a way, the loop filter tries to minimize the directional artifacts caused by block-by-block coding.
Here, portions of the encoder and the decoder have been illustrated as separate units. However, this is not intended to limit the scope of the present disclosure. As shown, the encoder 100 and decoder 150 include several common components. In some embodiments, the encoder and the decoder may be implemented as an integrated unit (e.g., one or more components of a encoder may be used for decoding. Furthermore, one or more components for the encoder and decoder may be implemented in one or more field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), processors, microcontrollers, or a combination thereof.
FIG. 2 is a block diagram of a portion of a video encoder according to an embodiment of the present disclosure. The intra prediction unit, also referred to as a “unified intra prediction unit 111” in this document takes a rectangular block of pixels as input, and predicts these pixels using the reconstructed pixels from the blocks already constructed, and a prediction direction according to Min, et al., “Unification of the Directional Intra Prediction Methods in TMuC,” ITU-T JCTVC-B100_revision02, Geneva, Switzerland, July 2010 (hereinafter referred to as “ITU-T JCTVC-B100_revision02”). There are different numbers of available intra-prediction modes that have a one-to-one mapping from the intra prediction direction for the various prediction units (e.g. 17 for 4×4; 34 for 8×8, 16×16, and 32×32; 5 for 64×64) as specified by the Unified Directional Intra Prediction (ITU-T JCTVC-B100_revision02). However, these are merely examples as the scope of the present disclosure is not limit thereto. The various intra-prediction modes will be described further below.
Following the prediction, a transform unit 120 applies a transform (e.g. DCT/DST) in both the horizontal and vertical directions. The transform (along horizontal and vertical directions) can either be the conventional DCT or proposed DST, depending on the intra-prediction mode. The transform is followed by the quantizer 122, which reduces the amount of information by dividing each transform coefficient by a particular number to reduce the quantity of possible values that a transform coefficient could have. Because this makes the values fall into a narrower range, this allows entropy coding to express the values more compactly and aid in compression.
In the intra-prediction unit 110, when intra prediction is performed from pixels along a direction as specified by the intra-prediction directional modes (e.g. “Test Model under Consideration,” ITU-T JCTVC-B205_draft002, Geneva, Switzerland, July 2010 (hereinafter referred to as “ITU-T JCTVC-B205_draft002”); and ITU-T JCTVC-B100_revision02), the intra-prediction modes can be divided into three categories. The present disclosure will describe the derivation for the new adaptive optimal transform for all of the following three categories:
1. Category 1 oblique modes (FIGS. 3A and 3B): Here prediction is performed entirely from the decoded pixels from either one of the following: the first row (e.g. top row) or the first column (e.g. left column) pixels. The vertical mode ‘0’ and the horizontal mode ‘1’ as described in ITU-T JCTVC-B205_draft002 are special cases of this oblique mode.
2. Category 2 oblique modes (FIG. 4): Here prediction is performed from both the first row (e.g. top row) and the first column (e.g. left column) pixels.
3. DC mode (FIG. 5): Here prediction is performed from an average value of the all the available decoded pixels similar to H.264/AVC also specified in ITU-T JCTVC-B205_draft002.
The three categories of intra-prediction directional modes will further be described with reference to FIGS. 6-11. FIG. 6 is a diagram illustrating the derivation of transforms for Category 1 oblique modes according to an embodiment of the present disclosure. Here, prediction is performed from the first row (e.g., top row) in the direction showed by the arrows. In another embodiment, the first row may not necessarily be the top row. In an alternative embodiment (similar to FIG. 3B), prediction may be performed from a first column (e.g. left row). Specifically a (dx, dy) pair denotes the horizontal and vertical distance respectively and can indicate the prediction direction.
Equation 1, below, assumes the Gauss-Markov model for the image pixels in the context of a one-dimension line: row or column (In the following discussion we use column when we mean one-dimension line):
xk=ρxk-1+ek [Eqn. 1]
where ρ is the correlation coefficient between the pixels, ek is a white-noise process with zero mean, variance of 1−ρ2, and the row/column index k=0 . . . N. Here x0 denotes the boundary pixel and x1 to xN are the pixels to be encoded. The correlation between pixels xk and xl is given by Equation 2:
Rkl=ρ|k-l| [Eqn. 2]
where kkl (also interpreted as Rk,l) denotes the correlation between pixels xk and xl, l and k denote the column indices. For the 2D image situation, we assume a separable model along the horizontal and vertical directions. Hence the correlation between pixels xij and xmn (also represented as xi,j and xm,n) is denoted according to Equation 3:
ρ|i-m|ρ|j-n|=ρ|i-m|+|j-n| [Eqn. 3]
where i denotes the row index of pixel xij, m denotes the row index of pixel xmn, j denotes the column index of pixel xij, and 12 denotes the column index of pixel xmn. In FIG. 6, the pixels x00, x01, x02, . . . x0N, and x10, x20, . . . xN0, denote the boundary pixels (e.g. the first row and first column) that have already been encoded. Pixels xij (i, j ∈ {1 . . . N}) denote the pixels to be encoded. Let the prediction for a pixel xij be given according to Equation 4 below:
{tilde over (x)}ij=x0(α+j) [Eqn. 4]
where {tilde over (x)}ij denotes the prediction for pixel xij, and α (a non-negative number) denotes the horizontal distance of pixel xij from the pixel x0(α+j), the pixel on the first row used for predicting xij. Note when α is not an integer, the pixel x0(α+j) is interpolated in any manner (e.g. from its adjacent two neighboring pixels as specified in ITU-T JCTVC-B205_draft002), but for simplicity, we keep the predictor as x0(α+j) only for analysis purposes. From the property of similar triangles, we can derive Equation 5:
Hence, the prediction error residue is given according to Equation 6:
eij=xij−{tilde over (x)}ij=xij−x0(α+j) [Eqn. 6]
The overall matrix for the error-residues for the N×N image block is given according to Equation 7:
E=X−{tilde over (X)} [Eqn. 7]
where X is the original N×N image block, and {tilde over (X)} is its prediction. Element ‘ij’ of Matrix E is given according to Equation 6.
Assuming the separable pixel model, we seek to find the optimal transforms in both the vertical and horizontal directions for the above prediction residue matrix. Specifically for finding the vertical transform of a column of E, a matrix which diagonalizes the autocorrelation matrix of the corresponding columns is determined. Similar for the horizontal transform for a particular row of E, we seek a matrix which diagonalizes the autocorrelation matrix of that particular row. For example, we first consider column ‘j’ of E according to Equation 8:
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