Abstract:
Pure transform-based technologies, such as the DCT or wavelets, can leverage a mathematical model based on few or one parameters to generate the expected distribution of the transform components&#39; energy, and generate ideal entropy removal configuration data continuously responsive to changes in video behavior. Construction of successive-refinement streams is supported by this technology, permitting response to changing channel conditions. Lossless compression is also supported by this process. The embodiment described herein uses a video correlation model to develop optimal entropy removal tables and optimal transmission sequence based on a combination of descriptive characteristics of the video source, enabling independent derivation of said optimal entropy removal tables and optimal transmission sequence in both encoder and decoder sides of the compression and playback process.

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
       [0001]    This application is a continuation of U.S. application Ser. No. 14/266,645, filed on Apr. 30, 2014, which is a non-provisional and claims benefit of a U.S. Provisional Application No. 61/818,419, filed on May 1, 2013. 
     
    
     BACKGROUND 
       [0002]    The present invention relates generally to compression of still image and moving video data, and more particularly to the application of calculation of statistics of the behavior of the quantized transform representation of the video from the measured variances and the measured correlations in pixel space. Once the statistical behavior of the video is modeled, the video streams can be collected into successive refinement streams (progressive mode), and the probabilities of the constructed symbols can be calculated for the purposes of entropy removal. The measured variances and correlations suffice to reconstruct the compressed video streams for any frame or group of frames. 
       SUMMARY 
       [0003]    In accordance with one aspect of the invention, a method is provided for the optimal rearrangement of components into a transmission stream based on the calculated variance of individual quantized transform components from the measured variance and correlation of the raw untransformed visual samples. 
         [0004]    A second aspect of the invention provides a method for the optimal calculation of entropy reduction tables for a transmission stream based on the calculated symbol probabilities based on the calculated probability distributions of individual quantized transform components. 
         [0005]    A final aspect of the invention provides a method for the parallel construction of transmission stream rearrangement, symbol construction and entropy tables between compressing apparatus and decompressing apparatus via communication of the measured variances and correlations of the raw untransformed visual samples. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0006]      FIG. 1  depicts a prior-art compressor decomposed into the steps of transformation, quantization, transmission order sequencing, symbol collection, and entropy removal. 
           [0007]      FIG. 2  depicts a prior-art compressor featuring per-block transmission order sequencing. 
           [0008]      FIG. 3  depicts a prior-art compressor featuring three forms of progressive transmission order encoding; spectral selection, successive refinement, and hierarchical. 
           [0009]      FIG. 4  depicts a prior-art compressor featuring various means of communication of entropy coding tables; pre-shared, in-band, and multiple tables. 
           [0010]      FIG. 5  depicts a typical embodiment of the current invention into a compression apparatus and a decompression apparatus. 
           [0011]      FIG. 6  depicts the steps typically required of a compression unit in order to perform block-by-block compression. 
           [0012]      FIG. 7  illustrated hierarchical subband decomposition and compression. 
           [0013]      FIG. 8  illustrates the calculations required to model per-quantized transform component variance from pixel variance and pixel correlation. 
           [0014]      FIG. 9  illustrates the calculations required to model per-quantized transform component variance from pixel variance and pixel correlation. 
       
    
    
     DETAILED DESCRIPTION 
       [0015]    As depicted in  FIG. 1 , most prior-art still image and motion video compression algorithms perform a similar sequence of steps: an input stream  1010  is transform coded  1020 , after which a process of motion estimation  1030  followed by equal-weight quantization  1040  or a process of visually-weighted quantization  1050  takes place, the resulting data is sequenced into transmission order  1060 , symbols are collected  1070 , and an entropy removal step  1080  results in a compressed data stream  1090 . The essential innovation of the current invention is in the area of prediction of statistical behavior, which influences the process of transmission order sequence, symbol collection, and entropy removal. The current invention does not address the topic of motion estimation. 
         [0016]    The JPEG zig-zag transmission order illustrated in  FIG. 2  is a standard prior-art means of sequencing quantized coefficients into transmission order using a fixed pattern based on the average of statistics collected across a variety of sample content. The JPEG zig-zag order of increasing probability of zero. The JPEG zig-zag order is applied to one fixed-size block of the image at a time. 
         [0017]    As depicted in  FIG. 3 , the prior-art JPEG-2000 standard implements various forms of progressive transmission, including spectral selection ( FIG. 3 a   ), successive refinement ( FIG. 3 b   ), and hierarchical ( FIG. 3 c   ).  FIG. 3 a    depicts a plurality of two 8×8 quantized transform blocks  3010  covered by a plurality of three spectral bands, of which spectral band  3020  is typical. Data collected within the band across all blocks  3030  and symbols are collected from the data within each band, which is then entropy coded and transmitted. 
         [0018]      FIG. 3 b    depicts a plurality of two 8×8 quantized transform blocks represented by a typical 2×2 entry  3110 . The 2×2 entry of eight-bit numbers is divided into two successive refinement bands of four-bit representation, one of which is depicted  3120 . The first four bits are collected across transform blocks into a transmission stream  3130  from which symbols will be collected and entropy coding will take place. The second four bits are similarly collected into transmission stream  3140 . 
         [0019]      FIG. 3 c    depicts a first-transmitted low-resolution image  3210 , followed by a second- transmitted medium-resolution image  3220 , and a final high-resolution image  3230 . Each separate-resolution image is used to create its own transmission stream. 
         [0020]      FIG. 4  depicts typical prior-art means of communicating entropy encoding statistics between compressing and decompressing apparatuses. It should be noted that these entropy statistics may be represented directly as a table of relative probabilities for the purposes of arithmetic encoding, or as Huffman tables. 
         [0021]    The original JPEG specification provides for a pre-defined entropy pre-shared encoding table as depicted in  FIG. 4 a   . A preshared table  4020  is known to compressor  4010 , and is used to generate the compressed data stream  4030 . The preshared table  4050  known to the decompressor  4040  is used to decompress the received data stream. Pre-shared tables are intended to provide good compression based on the collection of statistics for a large collection of images. 
         [0022]    As illustrated in  FIG. 4 b   , tables may be dynamically calculated and embedded in the transmission stream. A compressor  4110  calculates a table  4120 , which it then transmits in-band  4130  in the compressed transmission stream  4140 . The decompressor  4150  reads an in-band table  4160  and uses it to decompress the following compressed stream. This strategy enables better compression at the overhead cost of hundreds to thousands of bytes. 
         [0023]    Each JPEG-2000 progressive transmission approach described above in  FIG. 3  requires assembly of symbols over each progressive decomposition step (spectral band, successively bit representation, or resolution), giving different symbols and symbol distributions. As depicted in  FIG. 4 c   , a JPEG-2000 compressor  4210  calculates up to four entropy coding tables  4220  which it then transmits in-band  4230  in the compressed transmission stream  4240 . The decompressor  4250  reads the in-band tables  4260  and uses them, as selected by each progressive stream, to decompress the following compressed data. If the tables are calculated to reflect typical progressive stream behavior, the tables may potentially be reusable. 
         [0024]    Much effort has been expended in the incremental increase of efficiency in the communication of entropy coding statistics between compressing and decompressing apparatuses, but no significant advances can be claimed over the prior-are techniques described herein. The current invention discloses a far more efficient means of developing entropy tables independently in compressor and decompessor. 
         [0025]      FIG. 5  depicts a compression apparatus  5010  and a decompression apparatus  5020 . Said compression apparatus  5010  is fed a sequential stream of visual data  5110 , and factors said sequential stream of visual data  5110  into a plurality of multidimensional subblocks  5120 . Said plurality of multidimensional subblocks  5120  is processed singly or jointly by a correlation measurement unit  5130  to produce a flow of measured variance values and measured correlation values to the decompression apparatus  5210  and a duplicate flow of measured variance values and measured correlation values to a compressor  5140 . A compression unit  5150  uses said duplicate flow of measured variance values and measured correlation values to a compressor  5140  and said plurality of multidimensional subblocks  5120  to produce a compressed stream to the decompression apparatus  5220 . Said decompression apparatus  5020  is compressed of a decompressor  5310  which processes said flow of measured variance values and measured correlation values to the decompression apparatus  5210  and said compressed stream to the decompression apparatus  5220  to produce a plurality of reconstructed multidimensional subblocks  5320 . 
         [0026]      FIG. 6  depicts a decomposition of said compression apparatus  6010  into typical processing steps used to perform individual block-by-block compression. Said flow of measured variance values and measured correlation values to the decompression apparatus  5210  results in a set of variance values and correlation values in the x, y and z directions valid for one subblock  6110  of said plurality of multidimensional subblocks. Said set of kmeasured variance values and measured correlation values in the x, y and z directions valid for one subblock  6020  is processed through a step  6030  which calculates the variances for said quantized transform components of said one subblock  6110 . In a further processing step  6040 , said calculated variances for said quantized transform components of said one subblock from said step  6030  is used to calculate relative probabilities for each symbol. 
         [0027]    The quantized transform components of said one subblock  6110  of said plurality of multidimensional subblocks processed through a step  6120  to reorder quantized transform components into order of greatest probability of zero (lowest variance). Said step  6120  uses said calculated variances for said quantized transform components from said step  6030  to perform its sort processing. 
         [0028]    Said reordered quantized transform components are then processed through a step  6130  of collection of said reordered quantized transform components into symbols. Each said collected symbol is then processed through a step  6140  of entropy coding of said symbol into a short sequence of bits. Said step  6140  uses said calculated relative probabilities for each symbol from said step  6040  in its entropy-removing calculations. 
         [0029]    Said short sequence of bits is finally processed through an aggregation step  6150  to concatenate generated bit sequences into a transport stream. 
         [0030]      FIG. 7  depicts a typical implementation of the hierarchical type of progressive transmission. A sequential stream of visual data  7010  is subsampled from said sequential stream of visual data  5110 . Said subsam pled sequential stream of visual data  7010  is factored into a plurality of multidimensional subblocks  7020 . Said plurality of multidimensional subblocks  7020  is then processed subblock by subblock by said compression unit  5150  to produce a sequence of compressed bits for transmission. 
         [0031]    Once said subsampled sequential stream of visual data  7010  has been processed through said compression unit  5150 , a higher-resolution sequential stream of visual data less subband data  7110  may be processed. Said higher-resolution sequential stream of visual data less subband data  7110  is comprised of sequential stream of visual data  5110  where each and every coefficient comprising said subsampled sequential stream of visual data  7010  is set to 0 with a variance of 0. Said higher-resolution sequential stream of visual data less subband data  7110  is factored into a plurality of multidimensional subblocks  7120 . Said plurality of multidimensional subblocks  7120  is then processed subblock by subblock by said compression unit  5150  to produce a sequence of compressed bits for transmission. 
         [0032]      FIG. 8  illustrates the calculations required to model per-quantized transform component variance from pixel variance and pixel correlation. A matrix DCTx  8010  is comprised of the individual constants of discrete cosine transform convolution. Said matrix DCTx  8010  is shown with the discrete cosine transform of a 4×4 convolution, but may in practice be composed of any orthonormal transform. Similar matrices DCTv and DCTz (in the case of three-dimensional said multidimensional subblocks  7120 ) will assume the length of each dimension of the said multidimensional subblocks  7020 . 
         [0033]    A covariance matrix Apixel,x  8020  is composed of the multiplication of said measured pixel variance in the x direction by the autocorrelation matrix derived from said measured pixel correlation in the x direction. Similar matrices Apixel,y and Apixel,z (in the case of three-dimensional said multidimensional subblocks  7120 ) will utilize the measured pixel variance, pixel correlations and length of each dimension of the said multidimensional subblocks  7020 . 
         [0034]    DCT covariance matrix x  8030  is calculated as the product of said matrix DCTx  8010 , said covariance matrix Apixel,x  8020 , and the transpose of said matrix DCTx  8010 . 
         [0035]    The variance of the quantized transform component  8040  of index u,v,w within said multidimensional subblocks  7020 , a-2u,v,w, is calculated as the product of the trace of said DCT covariance matrix Ax  8030  with the trace of said DCT covariant matrix Av (and with the trace of said DCT covariant matrix Az if said multidimensional subblocks  7020  are three-dimensional) divided by the quantizer value for said quantized transform component  8040  of index u,v,w within said multidimensional subblocks  7020 . 
         [0036]      FIG. 9  illustrates the process of calculating symbol probabilities. The maximum number of bits subblock N MAX,u,v,w  9010  required to encode any said quantized transform component of index u,v,w within said quantized transform is calculated as the rounded-up integer of the logarithm base 2 of the product of the number of bits representing each pixel N1N, the square root of the product of the lengths of said multidimensional blocks divided by the quantizer Ou,v,w of said quantized transform component. 
         [0037]    The probability Pu,v,w(x==O)  9020  that any quantized transform component of index u,v,w within said quantized transform subblock is 0 is calculated from the Cumulative Distribution Function of a normal distribution with expectation of 0 and variance equal to that of said quantized transform component of index u,v,w within said quantized transform. 
         [0038]    The probability Pu,v,w(log2(x)==n)  9030  that any quantized transform component of index u,v,w within said quantized transform subblock has n bits in its representation is calculated from the Cumulative Distribution Function of a normal distribution with expectation of 0 and variance equal to that of said quantized transform component of index u,v,w within said quantized transform. 
         [0039]    A typical symbol Su,v,w(r, b)  9040  comprised of a run length of r zeros followed by a non-zero value of length b is calculated as the conditional probability the each symbol in the order of said rearrangement of said quantized transform component within said quantized transform subblock. The probability of the ith quantized transform component following quantized transform component index u,v,w within said quantized transform subblock being 0 is written P(u,v,w)+i(x==O). The probability of the rth quantized. 
         [0040]    While the present invention has been described in its preferred version or embodiment with some degree of particularity, it is understood that this description is intended as an example only, and that numerous changes in the composition or arrangements of apparatus elements and process steps may be made within the scope and spirit of the invention. In particular, rearrangement and recalculation of statistics may be made to support various modes of progressive transmission, including spectral banding or bitwise refinement. Further, pixel statistics may be measured and transmitted on a per-block or global basis, and may be measured in each dimension or averaged across all dimensions. Block sizes may also be taken to be as large as the entire frame, as would be typical when using the wavelet transform. 
         [0041]    With regard to the processes, systems, methods, heuristics, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes could be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. In other words, the descriptions of processes herein are provided for the purpose of illustrating certain embodiments, and should in no way be construed so as to limit the claimed invention. 
         [0042]    Accordingly, it is to be understood that the above description is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent to those of skill in the art upon reading the above description. The scope of the invention should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the arts discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the invention is capable of modification and variation and is limited only by the following claims. 
         [0043]    All terms used in the claims are intended to be given their broadest reasonable constructions and their ordinary meanings as understood by those skilled in the art unless an explicit indication to the contrary in made herein. In particular, use of the singular articles such as “a,” “the,” “said,” etc. should be read to recite one or more of the indicated elements unless a claim recites an explicit limitation to the contrary.