Source: http://www.google.com/patents/US7916952?ie=ISO-8859-1
Timestamp: 2014-09-30 11:52:22
Document Index: 114660242

Matched Legal Cases: ['art 2', 'art 10', 'art 10', 'art 10', 'art 9', 'art 10']

Patent US7916952 - High quality wide-range multi-layer image compression coding system - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsSystems, methods, and computer programs for high quality wide-range multi-layer image compression coding, including consistent ubiquitous use of floating point values in essentially all computations; an adjustable floating-point deadband; use of an optimal band-split filter; use of entire SNR layers...http://www.google.com/patents/US7916952?utm_source=gb-gplus-sharePatent US7916952 - High quality wide-range multi-layer image compression coding systemAdvanced Patent SearchPublication numberUS7916952 B2Publication typeGrantApplication numberUS 11/225,665Publication dateMar 29, 2011Filing dateSep 12, 2005Priority dateSep 14, 2004Also published asCA2575908A1, EP1790170A2, US8401315, US8666184, US20060071825, US20110194600, US20130163894, US20140177972, WO2006031737A2, WO2006031737A3, WO2006031737A8, WO2006031737A9Publication number11225665, 225665, US 7916952 B2, US 7916952B2, US-B2-7916952, US7916952 B2, US7916952B2InventorsGary DemosOriginal AssigneeGary DemosExport CitationBiBTeX, EndNote, RefManPatent Citations (13), Non-Patent Citations (7), Referenced by (16), Classifications (25) External Links: USPTO, USPTO Assignment, EspacenetHigh quality wide-range multi-layer image compression coding systemUS 7916952 B2Abstract Systems, methods, and computer programs for high quality wide-range multi-layer image compression coding, including consistent ubiquitous use of floating point values in essentially all computations; an adjustable floating-point deadband; use of an optimal band-split filter; use of entire SNR layers at lower resolution levels; targeting of specific SNR layers to specific quality improvements; concentration of coding bits in regions of interest in targeted band-split and SNR layers; use of statically-assigned targets for high-pass and/or for SNR layers; improved SNR by using a lower quantization value for regions of an image showing a higher compression coding error; application of non-linear functions of color when computing difference values when creating an SNR layer; use of finer overall quantization at lower resolution levels with regional quantization scaling; removal of source image noise before motion-compensated compression or film steadying; use of one or more full-range low bands; use of alternate quantization control images for SNR bands and other high resolution enhancing bands; application of lossless variable-length coding using adaptive regions; use of a folder and file structure for layers of bits; and a method of inserting new intra frames by counting the number of bits needed for a motion compensated frame.
BACKGROUND OF THE INVENTION This invention relates to compression of images, particularly sequences of digitized color video images.
For two decades, band-split technologies such as sub-band coding, low-pass/high-pass split pairs, and wavelet sub-band codings, have been applied to image compression. Recently notable is the sub-band discrete wavelet transforms (DWT) used in JPEG-2000 (see, for example, �JPEG2000, Image Compression Fundamentals, Standards, and Practice� by David S. Taubman and Michael W. Marcellin, Kluwer Academic Publishers 2002). The JPEG-2000 still image and intra-coded (i.e., no motion compensation) moving image coding system supports two �bi-orthogonal� wavelet classes in a sub-band configuration. A DWT 5/3 bi-orthogonal subband configuration is used for lossless compression, when exact bit match is required, but with only a small amount (typically 2.2:1) of compression. A DWT 9/7 bi-orthogonal subband configuration is more generally useful, and can provide a transform coding method for higher compression ratios, while preserving the �visual essence� of the image (although not bit-exact).
The fundamental merit of the DWT 9/7 bi-orthogonal subband configuration is the resemblance to a low-pass/high-pass filter pair. The �bi-orthogonality� refers to odd and even sample locations using low and high pass filters, respectively. This structure is then split into 4 sub-bands in JPEG-2000, with low horizontal and vertical (�low-low�), high horizontal and low vertical (�high-low�), low horizontal and high vertical (�low-high�), and high vertical and horizontal (�high-high�) subbands. This subband configuration can also utilize other band-split filter sets, and need not be structured bi-orthogonally at even/odd pixels. Any defined low band up-filter and high-band sum (with optional high-band filter) can yield a band-split suitable for use in compression coding.
A part of most image compression coding is the use of quantization. A �quantization parameter�, often known by its initials �QP�, is divided into localized frequency coefficients in essentially every common type of non-lossless compression system. To reconstruct a compressed image, the frequency coefficients are re-multiplied by the appropriate quantization parameter. Because of the integer nature of the quantized values, the reconstructed coefficients with vary by �half of the value of a step in the quantization parameter. For example, if the quantization parameter is 6, the reconstructed value will typically vary �3. Further, in order to increase the number of zero coefficients, which code most efficiently, a �deadband� is usually applied around zero. Thus, for example, even with a quantization parameter of 6, the value of 0 in a coefficient may span the range of �6 (rather than �3 without any deadband).
Variable length codes used in image compression range from extremely simple, such as run-length codes and delta codes, to moderately complex such as arithmetic codes. The purpose of the variable length code is to reduce the number of bits necessary to code the coefficient values compared to using a fixed number of bits capable of coding the maximum range. For example, if 16 bits are used because the values can range between �32767, but only a few values are larger than �127, then 8-bits could be used with one �escape� code reserved to indicate that the next value needs an additional 16-bits. Although the large �escaped� value then needs 24 bits (8+16), it is usually infrequent enough that the average coefficient coding size will be nearer to 8-bits than 24-bits. This methodology can be extended based upon the principle that very small values, and even zero itself, are much more likely than larger values of any size. In this way, a Huffman table attempts to use the shortest codes for small and likely values, and gradually longer codes for larger and less likely values.
JPEG-2000 does not offer motion compensation, since every frame stands alone. This is known as �intra� coding. MPEG-2, and many other similar coding systems, offer motion compensation, using blocks and motion vectors, for �inter� coding of images in a sequence of images. In such motion compensated coding systems, it is common practice to structure the motion blocks as a superset of the transform coding blocks. For example, in MPEG-2, the motion blocks are typically 16�16 pixels in size (16�8 for interlace), which encompasses four 8�8 DCT blocks (two for interlace). Thus, the block motion compensation structure is closely fitted to the DCT transform coding structure. MPEG-4, both as part 2 (original MPEG-4 video) and part 10 (also called the �Advanced Video Coder�), are structured similarly to MPEG-2 in these aspects.
MPEG-2 offers a rarely used �spatial scalable� option which allows an additional resolution increasing layer to be coded. The up-filter for this option differs greatly from the theoretically optimal truncated sinc. MPEG-2 also offers signal-to-noise-reduction (SNR) scalability, which is also rarely used. The basic structure of the SNR level of MPEG-2 is identical to basic MPEG-2�summing a correction to improve signal to noise in the resulting image. Neither spatial scalability nor SNR scalability are targeted at any specific goals, only general increase in resolution and SNR, respectively. Only a single SNR and a single spatial scalability level are defined in MPEG-2.
It has been common practice to mix floating-point and integer computations in reference compression coder software. For example, MPEG-2 and MPEG-4 use floating point reference DCT implementations, but integer processing for color processing, motion compensation, and most other aspects of the coding systems. JPEG-2000 uses a combination of integer and floating-point processing in its reference implementation. MPEG-4 part 10 uses an �integer transform� which combines the quantization and DCT transform steps into a single integer operation. Although the MPEG-4 part 10 implementation is not bit-exact invertible, the integer decoding is intended to exactly match between the encoder and decoder. This is a design feature of motion-compensated coding systems which the current inventor (along with David Ruhoff) has filed as patent application number 20020154693, entitled �High Precision Encoding and Decoding of Video Images�. The use of �exact match� decoding (that is, exactly matching between the decoder portion of the encoder, and all bitstream decoders) allows limited precision integer computations to be used without propagating errors when using motion compensation.
Relatively recently, Lucasfilm Industrial Light and Magic (a digital special-effects production company) and Nvidia (a maker of video cards for personal computers) have teamed up to create a standard known as �OpenExr�. OpenExr is an open �Extended Range� floating point representation featuring a 16-bit floating point representation having a sign bit, a 5-bit exponent, and a 10-bit mantissa. This representation provides sufficient precision for most image processing applications, as well as allowing an extended range for white and black. The 16-bit �half� floating representation provided by OpenExr can be directly mapped to standard 32-bit EEE floating point representation for easy interoperability.
SUMMARY OF THE INVENTION The invention encompasses systems, methods, and computer programs for high quality wide-range multi-layer image compression coding.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a block diagram of a prior art 9/7 DWT bi-orthogonal subband compression system.
DETAILED DESCRIPTION OF THE INVENTION Ubiquitous Floating Point
As image compression processing becomes more complex, as it does with the many-layered DWT 9/7 subband transform, the number of concatenated computational steps increase substantially, and may be on the order of thousands of processing steps. The common practice of mixing integer and floating point processing becomes problematic because the quantization errors from a sequence of computation steps can accumulate to significant errors. One aspect of the present invention is the consistent ubiquitous use of 32-bit floating point for pixel and transformed pixel values in all computations, except for initial RGB (red-green-blue channels) image file input and at the quantization step itself. In the preferred embodiment, the image input options include 32-bit floating point (e.g., �Tiff�, �DPX�, or �OpenExr� file formats), 16-bit �half� floating-point OpenExr, 16-bit integer Tiff, and 10-bit DPX (for compatibility) file formats. In the preferred embodiment, output format options for decoded images include all of the above except 10-bit DPX. All of these formats, if they are not already 32-bit floating point, are immediately converted to RGB 32-bit floating-point values upon input. The ubiquitous use of 32-bit floating point allows more complex computations to be concatenated during encoding and decoding without accumulating significant error. The error can be made insignificant in comparison to the quantization error inherent in quantized compression (even for very finely quantized, high quality compression), and in comparison to the noise floor in the original image. This aspect of the invention can be extended to 16-bit OpenExr floating point, to 24-bit floating point, to 48-bit, 64-bit, 128-bit, and all other useful floating point computational formats. With the exception of 16-bit OpenExr �half� floating point, all other common floating point formats have more-than 16-bits, and provide higher precision than is available with 8, 12, or 16-bit integer computations. Further, the increased precision of floating point eliminates the need for an �exact match� between the integer computations in the decoder subsystem within the encoder, and the, integer computations of the final decoder, in order to eliminate pixel error drift and accumulation during motion-compensated decoding. This is because the increased precision minimizes drift in pixel values which can commonly occur in the least-significant-bit roundoff of non-exactly-matching computational orders. With ubiquitous 32-bit floating point processing within an image compression system, thousands of computational steps can be applied to pixels between intra frames without any significant error accumulating in the least significant bit of a 16-bit pixel.
The more optimal band-split filter is similar at low resolution bands, in that the windowed sinc kernel to up-filter the low-band has a similar �extent� (i.e., the degree to which a low band transformed pixel affects one or more pixels in the next higher band). However, the high band difference image is not filtered, but is rather added directly to the up-filtered low-band. Thus, the high band difference pixels, at a given level, do not contain any extent beyond their single pixel. However, at the next resolution band up, the pixels become the next low band, and the windowed sinc kernel extends their influence according to its size. The final high resolution level, or any lower resolution level which is displayed or saved, has the high band pixels with a single-pixel extent. Thus, the high-band difference pixels in the more optimal band-split filter are single-pixel differences, with each adjusting a single pixel, and not influencing adjacent pixels at its level. Because of this, there is not strict equivalence between the high bands of the more optimal band-split filter transform and high bands of the DWT 9/7 bi-orthogonal subband coding. Even if the same quantization value is used for the high bands of the more optimal band-split filter structure and the DWT 9/7 transform, the appearance is different because of the 9-pixel high-band filter extent of the DWT 9/7 transform versus the single-pixel high-band extent of the more optimal band-split filter structure. Because of this, the choice of whether to use the DWT 9/7 transform, or the more optimal band-split filters, should take into consideration the difference in appearance of these transforms. Even if both types of transform are quantized the same, the appearance is different. Thus, the transform with the lowest number of bits at a given quantization is not strictly the only criterion for selecting between the transforms.
The property of pixel transform coefficient influence or �extent� is shared among DCT and other transform codes. For example, each of the 8�8 DCT coefficients influences an 8-wide by 8-tall pixel region when inverted. Similarly, a single pixel value in the original 8�8 pixel block affects all of the 8�8 coefficients in the transformed coefficients of the DCT. The main difference between the DCT and the filters described here, both more optimal band-split and the DWT 9/7 bi-orthogonal subband, is that the region of influence of the latter is approximately centered (plus and minus one pixel) about the current coefficient, whereas the DCT coefficients are associated only with a fixed-position block. Thus, a pixel at the edge of an 8�8 DCT block does not affect the adjacent block. A quantized DCT can thus exhibit block-edge artifacts, whereas the more optimal transform coding and DWT 9/7 bi-orthogonal subband do not degrade in this way. Instead, the degradation of quantized DWT 9/7 low and high bands appears as an unnatural �footprint� of the low (7-tap) and high (9-tap) synthesis kernels, as concatenated over all levels. In contrast, while the more optimal band-split filter may have an unnatural appearance due to the low-band up-filter kernel, concatenated over all levels, it only has local pixel inaccuracies, limited to single pixels, at the highest viewed level. Note that the more optimal band-split filter's low-band up-filter windowed sinc filter is often relatively invisible, since it preserves the soft low-band image appearance, without adding undue kernel ringing (if truncated at a relative small size, such as �3 pixels or less). Thus, the more optimal band-split filter exhibits softness due to quantization, as well as overall pixel errors due to quantization, but does not exhibit as much ringing or other kernel footprint appearance artifacts as the DWT 9/7 transform.
It should also be noted that a less optimal phase structure centered over a pixel can be used with filters more similar to a truncated (also called �windowed�) sinc than the DWT 9/7 subband wavelet filter kernel. The DWT 9/7 wavelet kernel is bi-orthogonal, which restricts the filter kernels to specific bi-orthogonal wavelet pair sets. When using a more optimal band-split filter which is more similar to a truncated sinc, the sub-band structure with low-low, high-low, low-high, and high-high cannot be used. However, the less optimal filter phase with a more optimal filter kernel, providing a better band split than the DWT 9/7 bi-orthogonal subband kernel, also offers benefits in some applications. However, the more optimal band-split filter phase structure is usually preferable.
Note that some target weighting methods involve per-pixel targeting, such as the inverse of the sum of decoded red, green, and blue to target dark pixels. Such weighting methods require a similar process in the decoder for each pixel. Other weighting methods, such as weightings to the SNR difference values, may be applied in the encoder with a different weighting at every pixel, but need have no inverse weighting process applied during decoding, since the encoded weights are intended to be left �baked in� during decoding as if they were inherent to the original image signal. Still other methods, such as wide-range scaling of pixel differences, or use of regional adjustments to the quantization value, require regional processing in the decoder to properly re-adjust the scale of the differences, or to regionally adjust the quantization value for decoding so that it matches the quantization value used during encoding. Which method or methods are used can be signaled to the decoder, and each method is useful for all portions of the transform-coded values, including SNR, the motion compensated difference image at any layer, and resolution enhancing-layer encoding of intra images or motion-compensated difference images. It is also useful to mix-and-match all of these targeting methods in each frame, to optimize the application of these methods. However, the main intent of these methods is to apply one or more method to each SNR layer to target specific image features for SNR improvement.
Efficiency can be gained in some applications by concentrating coding bits in regions of interest. Another way to view this is that bits used to create details and nuances in regions of little interest are being wasted. For example, many television receivers use �overscan� which places the edge of an image outside of the viewable display area, making those outside portions invisible. Overscan in the range of 4% to 8% is typical. However, some receivers, and computer displays showing decompressed images, do not overscan. It is thus useful to create the full image, but to concentrate coding bits on regions away from the frame edges.
Thus, there is a natural choice for regions of lower interest between coarser quantization versus reduced amplitude, versus a combination of both coarser quantization and reduced amplitude. Information is conveyed to the decoder when using regional quantization adjustments, and/or regional deadband, and/or regional amplitude scaling and re-scaling. No additional information need by conveyed by regional weightings which use the quantization and deadband which would have been present if no weighting was applied, and thus where such weightings are intended to be reproduced during decoding as if they were inherent to, or �baked in�, to the original image signal.
It is common practice to apply a single quantization value to an SNR layer. Larger coded differences (compression coding errors) generate more coded bits than smaller coded errors, in a relatively uniform manner. In those cases where quantization varies by region (usually on �macroblock� boundaries), the common practice is adjustment according to variable rate control to meet a specific target for the number of bits being generated (usually to meet a realtime buffer and delay constraint). Such methods for applying quantization to SNR improvement layers provide limited control over the location and amount of the improvement. Further, there is an assumption that the amount of difference is relatively uniform over the image, or only varies if the quantization is varying for the underlying coding (usually via rate-control for the layers below the SNR layer). However, some coding difference errors are due to concatenated precision errors resulting from quantization during computation (not quantization for compression). In a deeply-layered transform coding system, such as DWT 9/7 subband coding, or deeply-stacked band-split transform coding, concatenated errors can accumulate using floating-point or fixed-point transform processing due to the large number of computational steps. Such computational difference errors add to quantization difference errors at every layer, resulting in a statistical variation of the error, with occasional large errors at several times the standard deviation (�sigma�). In order to reduce such maximum worst-case difference coding errors, improved precision, via decreased quantization, can be applied to the regions containing such errors.
In a YUV representation (also called YPbPr and YCbCr), the U (Pr Cr) and V (PbCb) channels are R�Y and B�Y, respectively. These R and B values are non-linear due to gamma adjustment. However, the R�Y and B�Y values are not perceptually weighted, but are rather weighted by the amount of color (saturation), and are offset to a mid-gray (half of white maximum) value. These U and V channels are thus not weighted perceptually by the gamma function, as is the Y (luminance) signal, or as are the R, G, and B values in coding systems which support direct RGB compression coding.
As with SNR layers, motion compensation is intended to be applied to specific layers, usually at medium to high resolution. Motion compensation, in general, is the displacement of a region (usually rectangular or square) according to a motion vector (or more than one motion vector, in the case of �B� frames), from a previous (or subsequent) frame, for subtraction with a region in the current frame.
Another practical issue involves silicon image sensors. For silicon sensors, and other types of electronic image sensors, it is common to have manufacturing variation in pixel sensitivity for each pixel location. This is called �fixed pattern noise�, since the variation does not move or change because it is attached to each pixel. Such noise is not affected by whether the image is in focus, but is more easily revealed if the image is not in focus. It is best to average captured images over a number of frames in order to determine the fixed pattern noise at each pixel at each brightness. This can be done by imaging a set of full-frame colors and brightnesses, or by any other method of ensuring that each pixel location is tested at all brightness levels to which the sensor is responsive. The average thus determined can then be removed from every original image generated by that specific sensor by appropriate computation, such as subtraction.
Note that film perforation registration is imprecise, and differential film shrinkage occurs. Scanned film therefore exhibits �gate weave� and other motion variations not inherent in the original image. Such distortions and unwanted motions are treated as image motion by the present invention, and the removal of these distortions and unwanted motions is the subject of known techniques. If such distortions and unwanted motions are removed, the resulting image more closely matches the original image in steadiness and actual motion, assuming that the algorithms applied function properly. Note, however, that the processes which attempt to restore the correct motion to the film image also move and alter the fixed pattern noise, making its removal impossible. Thus, another aspect of this invention is the process whereby fixed pattern noise is reduced or removed before using other technologies to steady the scanned film image, prior to compression with this invention.
Another practical problem occurs due to lens flare, where bright regions of the image �spill� into darker regions. This problem can be eliminated by the use of full-frames of color and/or brightness. The problem can be reduced by imaging a multi-color, multi-brightness chart using known �flare correction� techniques which attempt to model the lens flare of a given lens, and then subtracting out the resulting brightness and color interference due to that len's flare.
Another aspect of this invention is the use of one or more full-range low bands, meaning that at least the lowest band of a band-split hierarchy utilizes floating-point representation to provide wide-range and high precision. All higher bands can be coded with quantized integers, which are decoded using dequantized floating point values which form high bands for the synthesis of higher resolution bands. The use of OpenExr half 16-bit floating point representation provides a relatively compact way of directly storing these low bands, taking advantage of the internal lossless �Piz� compression (Huffman, cluster-table, Haar-wavelet).
Once integers are obtained by dividing the quantization parameter into the band-split-transformed pixel value, they are efficiently coded in a lossless (bit-exact) manner. Common practice uses a single variable-length coding (�vlc�) table such as Huffman or Arithmetic coding. With Huffman coding, each value is coded separately. With Arithmetic coding, multiple values are coded together. It is also common practice to extend a simple static table to multiple static tables, each for different purposes. Further, there exists a practice of �adaptive� variable length coding, where several tables are available, and the most efficient one can be chosen and signaled to the decoder. It is also possible to send a table from the encoder to the decoder, based upon a specific efficient coding for that data. These adaptive and signaled practices are not common, but they have been utilized, sometimes with a reasonable compression efficiency gain.
It is another novel aspect of this invention that the lossless variable-length coding of band-split transform pixel coefficients are coded using adaptive regions. These regions can have arbitrary size and shape, and are selected solely on lossless coding efficiency (i.e., minimum bit-size). The size and shape of these regions is not limited to any particular size and shape, unlike previous vlc coding methods. In this aspect of the invention, a number of �vlc� tables are available which are tested against all selected region sizes and shapes. Further, downloadable vlc tables can be utilized. For each selected region size and shape, all of the statically-available vlc tables are tested for bit-size. The size, shape, and vlc table which has the smallest bit-size are then signaled to the decoder.
For example, the SNR layers describe above, which target specific features, may encounter large regions where only sparse data is needed. For such large regions, the ability to adapt the region sizes and shapes (using the best vlc for that region) to the need for coded data in those regions greatly reduces the bit-coding overhead of high-resolution SNR layers. Using this aspect of the invention, many SNR layers can be applied, even at very high resolution (such as 4 k resolution) with only a small bit-size overhead for having such layers in regions in which they are not actively targeting image improvement. Further, if such an SNR layer finds little �work� to do, meaning when none of the targeted image data is present, then there is little overhead for having the layer present. This is a significant benefit to this adaptive region size, shape, and vlc selection method.
In addition, at the start of each sequence (typically a scene), as well as at optional convenient subsequent frames, a header file can be stored. The header file contains typical header information or metadata, such as the number of frames, the distance between intra frames (if motion compensation is active, or a distance of �one� to deactivate motion compensation), the resolution of each layer, the presence or absence of each optional layer, the overall quantization values for each layer present, the size of the deadband for each layer, and other required per-sequence information. It is usual to include a header file when inserting intra-frames at less frame distance than a specified �intra-stride� parameter value.
FIG. 8 is a diagram showing one possible layered file structure in accordance with this aspect of the invention. Each entry in the example shown is in the form of a type identifier (�Folder� or �File�) and an item name (e.g., �low-low_floating_point�); however, in implementation, the type identifier generally is implicit in the directory structure of a computer file system. In this example, a root folder 800 includes one or more header files 802 as desecribed above, and then a series of folder 804 representing a separable aspect of the compressed image sequence. Each folder 804 contains files 806 that contain the actual bits corresponding to each frame of the specific separable aspect of the compressed image sequence represented by the folder structure.
Patent CitationsCited PatentFiling datePublication dateApplicantTitleUS5250948 *Dec 19, 1991Oct 5, 1993Eastman Kodak CompanyHigh level resolution enhancement for dual-range A/D conversionUS5414469 *Oct 31, 1991May 9, 1995International Business Machines CorporationMotion video compression system with multiresolution featuresUS5444575 *Jan 21, 1994Aug 22, 1995Hitachi America, Ltd.Method for recording digital data using a set of heads including a pair of co-located heads to record data at a rate lower than the full recording rate possible using the set of headsUS5923814 *Aug 15, 1997Jul 13, 1999Hitachi America, Ltd.Methods and apparatus for performing video data reduction operations and for concealing the visual effects of data reduction operationsUS6351491 *Jun 15, 2000Feb 26, 2002Sarnoff CorporationApparatus and method for optimizing the rate control for multiscale entropy encodingUS6549674 *Oct 12, 2000Apr 15, 2003Picsurf, Inc.Image compression based on tiled wavelet-like transform using edge and non-edge filtersUS6885395 *Sep 7, 2000Apr 26, 2005Eastman Kodak CompanySelectively adjusting the resolution levels or the quality levels of digital images stored in a digital camera memoryUS20020057850Nov 21, 2001May 16, 2002Sirohey Saad A.Method and apparatus for transmission and display of a compressed digitized imageUS20050091051 *Mar 10, 2003Apr 28, 2005Nippon Telegraph And Telephone CorporationDigital signal encoding method, decoding method, encoding device, decoding device, digital signal encoding program, and decoding programUS20060071825 *Sep 12, 2005Apr 6, 2006Gary DemosHigh quality wide-range multi-layer image compression coding systemUS20070160305 *Jan 10, 2007Jul 12, 2007Gary DemosEfficient bit-exact lossless image coding residual systemWO1992022166A1Jun 3, 1992Dec 10, 1992Qualcomm IncAdaptive block size image compression method and systemWO2003065732A2Jan 27, 2003Aug 7, 2003Quvis IncDigital image processor* Cited by examinerNon-Patent CitationsReference1Adetti/ISCTE JPEG, "Lossless Coding of Floating Point IEEE754 Data," JPEG Conference, Browborough, JPEG Forum, Ltd., Mar. 31, 2004.2Gamito et al.. "Lossless Coding of Floating Point Data with JPEG 2000, Part 10" JPEG 2000 Theory and Applications Special Session of the 49th SPIE Annual Meeting, Aug. 2, 2004, Denver, Colorado.3International Search Report dated Jul. 19, 2006 for corresponding PCT patent application serial No. PCT/US2005/032428.4Kingsbury N. Ed, Institute of Electrical and Electronics Engineers: "Design of Q-shift complex wavelets for image processing using frequency domain energy minimization" Proceedings 2003 International Conference on Processing, ICIP-2003, Barcelona, Spain, Sep. 14-17, 2003, International Conference on Image Processing, NY, NY, vol. 2 of 3, Sep. 14, 2003.5Majid Rabbani and Rajan Joshi et al. An overview of the JPEG2000 Still Image Compression Standard-Rev. 1:, JPEG Conference, Crowborough: JPEG Forum Ltd., GB, Jul. 19, 2001, paragraph [02.2].6Majid Rabbani and Rajan Joshi et al. An overview of the JPEG2000 Still Image Compression Standard�Rev. 1:, JPEG Conference, Crowborough: JPEG Forum Ltd., GB, Jul. 19, 2001, paragraph [02.2].7Wohlberg et al., "Extending the JPEG 2000 image coding standard to support floating point data," ISO/IEC JTC1/SC29/WG1 Document #3020, Jul. 2, 2003, Strasbourg, France.Referenced byCiting PatentFiling datePublication dateApplicantTitleUS8139887 *Jun 10, 2008Mar 20, 2012Sony CorporationImage-signal processing apparatus, image-signal processing method and image-signal processing programUS8149469 *Jul 31, 2008Apr 3, 2012Canon Kabushiki KaishaImage reading apparatus and image reading methodUS8265139 *Dec 19, 2008Sep 11, 2012Cisco Technology, Inc.Packet comparatorUS8351500 *Jan 15, 2009Jan 8, 2013Nec CorporationEntropy encoder, video coding apparatus, video coding method and video coding programUS8401315 *Feb 9, 2011Mar 19, 2013Gary DemosFile and folder structure for layered compressed image sequencesUS8406542 *Nov 10, 2009Mar 26, 2013Sony CorporationImage decoding apparatus, image decoding method and computer programUS8666184 *Feb 20, 2013Mar 4, 2014Gary DemosVariable length coding systemUS20080304758 *Jun 10, 2008Dec 11, 2008Sony CorporationImage-signal processing apparatus, image-signal processing method and image-signal processing programUS20090034025 *Jul 31, 2008Feb 5, 2009Canon Kabushiki KaishaImage reading apparatus and image reading methodUS20090180536 *Jan 15, 2009Jul 16, 2009Kensuke ShimofureEntropy encoder, video coding apparatus, video coding method and video coding programUS20100118981 *May 29, 2008May 13, 2010Thomson LicensingMethod and apparatus for multi-lattice sparsity-based filteringUS20100119167 *Nov 10, 2009May 13, 2010Sony CorporationImage decoding apparatus, image decoding method and computer programUS20100128803 *Jun 3, 2008May 27, 2010Oscar Divorra EscodaMethods and apparatus for in-loop de-artifacting filtering based on multi-lattice sparsity-based filteringUS20100309987 *Jul 31, 2009Dec 9, 2010Apple Inc.Image acquisition and encoding systemUS20110194600 *Feb 9, 2011Aug 11, 2011Gary DemosFile and folder structure for layered compressed image sequencesUS20140064380 *Sep 27, 2013Mar 6, 2014Zoran (France) S.A.Frame buffer compression for video processing devices* Cited by examinerClassifications U.S. Classification382/232, 341/50International ClassificationG06K9/46, H03M7/00, G06K9/36Cooperative ClassificationH04N19/0009, H04N19/00321, H04N19/0023, H04N19/00169, H04N19/0026, H04N19/00824, H04N19/00818, H04N19/0006, H04N19/00781, H03M7/24, H04N19/00418, H04N19/00903European ClassificationH04N7/26H30E5A, H04N7/26J14, H03M7/24, H04N7/26H30C2J, H04N7/26A8Y, H04N7/26A6U, H04N7/26H30D1, H04N7/26A4QRotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google