Summary form only given. We describe an algorithm that gives a progression of compressed versions of a single image. Each stage of the progression is a lossy compression of the image, with the distortion decreasing in each stage, until the last image is losslessly compressed. Both compressor and decompressor make use of earlier stages to significantly improve the compression of later stages of the progression. Our algorithm uses vector quantization to improve the distortion at the beginning of the progression, and adapts Ziv and Lempel's algorithm to make it efficient for progressive encoding.
{"title":"Progressive Ziv-Lempel encoding of synthetic images","authors":"Derek Greene, M. Vishwanath, F. Yao, Tong Zhang","doi":"10.1109/DCC.1997.582099","DOIUrl":"https://doi.org/10.1109/DCC.1997.582099","url":null,"abstract":"Summary form only given. We describe an algorithm that gives a progression of compressed versions of a single image. Each stage of the progression is a lossy compression of the image, with the distortion decreasing in each stage, until the last image is losslessly compressed. Both compressor and decompressor make use of earlier stages to significantly improve the compression of later stages of the progression. Our algorithm uses vector quantization to improve the distortion at the beginning of the progression, and adapts Ziv and Lempel's algorithm to make it efficient for progressive encoding.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114470169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. With conditional coding a new technique is presented that encodes equally likely symbols of an input alphabet A (|A|=m) efficiently. The code consists of bitstrings with size n=[log/sub 2/(m)] and (n+1) and is a prefix code. The encoding needs only one comparison, one shift, and one addition per encoded symbol. Compared to the theoretical limit the method loses only at most 0.086071... bits per encoding and 0.057304... bits in average. Opposed to radix conversion (which achieves the theoretical limit) the algorithm works without multiplication and division and does not need a single-bit writing loop or bitstring arithmetic in the encoding step. Therefore it works a lot faster than radix conversion and can easily be implemented in hardware. The decoding step has the same properties. Encoding and decoding can be exchanged for better adaption to the code alphabet size.
只提供摘要形式。利用条件编码，提出了一种有效编码输入字母a (| a |=m)等可能符号的新方法。该码由大小为n=[log/sub 2/(m)]和(n+1)的位串组成，是前缀码。编码只需要对每个编码符号进行一次比较、一次移位和一次加法。与理论极限相比，该方法最多损失0.086071…每编码位和0.057304…平均位数。与基数转换(达到理论极限)相反，该算法不需要乘法和除法，也不需要在编码步骤中进行单位写入循环或位串算术。因此，它比基数转换快得多，并且可以很容易地在硬件中实现。解码步骤具有相同的属性。编码和解码可以交换，以更好地适应代码字母表的大小。
{"title":"Encoding of intervals with conditional coding","authors":"U. Graf","doi":"10.1109/DCC.1997.582097","DOIUrl":"https://doi.org/10.1109/DCC.1997.582097","url":null,"abstract":"Summary form only given. With conditional coding a new technique is presented that encodes equally likely symbols of an input alphabet A (|A|=m) efficiently. The code consists of bitstrings with size n=[log/sub 2/(m)] and (n+1) and is a prefix code. The encoding needs only one comparison, one shift, and one addition per encoded symbol. Compared to the theoretical limit the method loses only at most 0.086071... bits per encoding and 0.057304... bits in average. Opposed to radix conversion (which achieves the theoretical limit) the algorithm works without multiplication and division and does not need a single-bit writing loop or bitstring arithmetic in the encoding step. Therefore it works a lot faster than radix conversion and can easily be implemented in hardware. The decoding step has the same properties. Encoding and decoding can be exchanged for better adaption to the code alphabet size.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116050276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pattern-matching based document compression systems rely on finding a small set of patterns that can be used to represent all of the ink in the document. Finding an optimal set of patterns is NP-hard; previous compression schemes have resorted to heuristics. We extend the cross-entropy approach, used previously for measuring pattern similarity, to this problem. Using this approach we reduce the problem to the fixed-cost k-median problem, for which we present a new algorithm with a good provable performance guarantee. We test our new algorithm in place of the previous heuristics (First Fit, with and without generalized Lloyd's (k-means) postprocessing steps). The new algorithm generates a better codebook, resulting in an overall improvement in compression performance of almost 17%.
{"title":"A codebook generation algorithm for document image compression","authors":"Qin Zhang, J. Danskin, N. Young","doi":"10.1109/DCC.1997.582053","DOIUrl":"https://doi.org/10.1109/DCC.1997.582053","url":null,"abstract":"Pattern-matching based document compression systems rely on finding a small set of patterns that can be used to represent all of the ink in the document. Finding an optimal set of patterns is NP-hard; previous compression schemes have resorted to heuristics. We extend the cross-entropy approach, used previously for measuring pattern similarity, to this problem. Using this approach we reduce the problem to the fixed-cost k-median problem, for which we present a new algorithm with a good provable performance guarantee. We test our new algorithm in place of the previous heuristics (First Fit, with and without generalized Lloyd's (k-means) postprocessing steps). The new algorithm generates a better codebook, resulting in an overall improvement in compression performance of almost 17%.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121320020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A fast and efficient quantization technique is described. It is fixed-length, robust to bit errors, and compatible with most current compression standards. It is based on entropy-constrained quantization and uses the well-known and efficient Viterbi algorithm to force the coded sequence to be fixed-rate. Run-length coding techniques are used to improve the performance at low encoding rates. Simulation results show that it can achieve performance comparable to that of Huffman coded entropy-constrained scalar quantization with computational complexity increasing only linearly in block length.
{"title":"A fixed-rate quantizer using block-based entropy-constrained quantization and run-length coding","authors":"Dongchang Yu, M. Marcellin","doi":"10.1109/DCC.1997.582054","DOIUrl":"https://doi.org/10.1109/DCC.1997.582054","url":null,"abstract":"A fast and efficient quantization technique is described. It is fixed-length, robust to bit errors, and compatible with most current compression standards. It is based on entropy-constrained quantization and uses the well-known and efficient Viterbi algorithm to force the coded sequence to be fixed-rate. Run-length coding techniques are used to improve the performance at low encoding rates. Simulation results show that it can achieve performance comparable to that of Huffman coded entropy-constrained scalar quantization with computational complexity increasing only linearly in block length.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123896360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Off-centered, two-sided geometric distributions of the integers are often encountered in lossless image compression applications, as probabilistic models for prediction residuals. Based on a recent characterization of the family of optimal prefix codes for these distributions, which is an extension of the Golomb (1966) codes, we investigate adaptive strategies for their symbol-by-symbol prefix coding, as opposed to arithmetic coding. Our strategies allow for adaptive coding of prediction residuals at very low complexity. They provide a theoretical framework for the heuristic approximations frequently used when modifying the Golomb code, originally designed for one-sided geometric distributions of non-negative integers, so as to apply to the encoding of any integer.
{"title":"On adaptive strategies for an extended family of Golomb-type codes","authors":"G. Seroussi, M. Weinberger","doi":"10.1109/DCC.1997.581993","DOIUrl":"https://doi.org/10.1109/DCC.1997.581993","url":null,"abstract":"Off-centered, two-sided geometric distributions of the integers are often encountered in lossless image compression applications, as probabilistic models for prediction residuals. Based on a recent characterization of the family of optimal prefix codes for these distributions, which is an extension of the Golomb (1966) codes, we investigate adaptive strategies for their symbol-by-symbol prefix coding, as opposed to arithmetic coding. Our strategies allow for adaptive coding of prediction residuals at very low complexity. They provide a theoretical framework for the heuristic approximations frequently used when modifying the Golomb code, originally designed for one-sided geometric distributions of non-negative integers, so as to apply to the encoding of any integer.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127664618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vector quantization (VQ) is a source coding methodology with provable rate-distortion optimality. However, despite more than two decades of intensive research, VQ theoretical promise is yet to be fully realized in image compression practice. Restricted by high VQ complexity in dimensions and due to high-order sample correlations in images, block sizes of practical VQ image coders are hardly large enough to achieve the rate-distortion optimality. Among the large number of VQ variants in the literature, a technique called address VQ (A-VQ) by Nasrabadi and Feng (1990) achieved the best rate-distortion performance so far to the best of our knowledge. The essence of A-VQ is to effectively increase VQ dimensions by a lossless coding of a group of 16-dimensional VQ codewords that are spatially adjacent. From a different perspective, we can consider a signal source that is coded by memoryless basic VQ to be just another signal source whose samples are the indices of the memoryless VQ codewords, and then induce the problem of lossless compression of the VQ-coded source. If the memoryless VQ is not rate-distortion optimal (often the case in practice), then there must exist hidden structures between the samples of VQ-coded source (VQ codewords). Therefore, an alternative way of approaching the rate-distortion optimality is to model and utilize these inter-codewords structures or correlations by context modeling and conditional entropy coding of VQ indexes.
矢量量化(VQ)是一种具有可证明的率失真最优性的源编码方法。然而，尽管经过了二十多年的深入研究，VQ的理论前景尚未在图像压缩实践中得到充分实现。由于VQ在维度上的高复杂度和图像中的高阶样本相关性的限制，实际VQ图像编码器的块大小很难大到足以实现率失真的最优性。在文献中大量的VQ变体中，Nasrabadi和Feng(1990)的一种称为地址VQ (a -VQ)的技术达到了迄今为止我们所知的最佳速率失真性能。a -VQ的本质是通过对一组空间相邻的16维VQ码字进行无损编码，有效地增加VQ的维数。从另一个角度来看，我们可以把一个由无记忆基本VQ编码的信号源看作是另一个信号源，其样本是无记忆基本VQ码字的索引，从而引出VQ编码的信号源的无损压缩问题。如果无记忆VQ不是速率失真最优(在实践中经常出现这种情况)，那么在VQ编码源(VQ码字)的样本之间一定存在隐藏结构。因此，接近率失真最优性的另一种方法是通过上下文建模和VQ索引的条件熵编码来建模和利用这些码字间结构或相关性。
{"title":"Conditional entropy coding of VQ indexes for image compression","authors":"Xiaolin Wu, Jiang Wen, W. H. Wong","doi":"10.1109/DCC.1997.582058","DOIUrl":"https://doi.org/10.1109/DCC.1997.582058","url":null,"abstract":"Vector quantization (VQ) is a source coding methodology with provable rate-distortion optimality. However, despite more than two decades of intensive research, VQ theoretical promise is yet to be fully realized in image compression practice. Restricted by high VQ complexity in dimensions and due to high-order sample correlations in images, block sizes of practical VQ image coders are hardly large enough to achieve the rate-distortion optimality. Among the large number of VQ variants in the literature, a technique called address VQ (A-VQ) by Nasrabadi and Feng (1990) achieved the best rate-distortion performance so far to the best of our knowledge. The essence of A-VQ is to effectively increase VQ dimensions by a lossless coding of a group of 16-dimensional VQ codewords that are spatially adjacent. From a different perspective, we can consider a signal source that is coded by memoryless basic VQ to be just another signal source whose samples are the indices of the memoryless VQ codewords, and then induce the problem of lossless compression of the VQ-coded source. If the memoryless VQ is not rate-distortion optimal (often the case in practice), then there must exist hidden structures between the samples of VQ-coded source (VQ codewords). Therefore, an alternative way of approaching the rate-distortion optimality is to model and utilize these inter-codewords structures or correlations by context modeling and conditional entropy coding of VQ indexes.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130441020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. Video samples usually are predicted from coded versions of nearby samples sent either earlier in the same frame or in the previous frame. Analysis of the human vision system (HVS) suggests that we may not need to correct values of residuals that do not exceed a perceptual threshold sometimes referred to in the literature of perception as the just-noticeable-distortion (JND). The ideal JND provides each pixel being coded with a threshold level below which discrepancies are perceptually distortion-free. Also of interest is the rate control analysis of noticeable, above threshold distortions that inevitably result at low bit rates. Because facsimile-based video compression (FBVC) processing is done in the spatio-temporal pixel domain, we can exploit the local psycho-perceptual properties of the HVS. Our proposed rate control algorithms are distinguished by being computationally economical, transform-free, devoid of block-based artifacts, and capable of easily providing a constant bit rate video stream.
{"title":"Perceptual rate control algorithms for fax-based video compression","authors":"Yi-Jen Chin, T. Berger","doi":"10.1109/DCC.1997.582086","DOIUrl":"https://doi.org/10.1109/DCC.1997.582086","url":null,"abstract":"Summary form only given. Video samples usually are predicted from coded versions of nearby samples sent either earlier in the same frame or in the previous frame. Analysis of the human vision system (HVS) suggests that we may not need to correct values of residuals that do not exceed a perceptual threshold sometimes referred to in the literature of perception as the just-noticeable-distortion (JND). The ideal JND provides each pixel being coded with a threshold level below which discrepancies are perceptually distortion-free. Also of interest is the rate control analysis of noticeable, above threshold distortions that inevitably result at low bit rates. Because facsimile-based video compression (FBVC) processing is done in the spatio-temporal pixel domain, we can exploit the local psycho-perceptual properties of the HVS. Our proposed rate control algorithms are distinguished by being computationally economical, transform-free, devoid of block-based artifacts, and capable of easily providing a constant bit rate video stream.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125912440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. Progressive, adaptive and hierarchical modes are desirable image coding features. This paper presents a quadtree-pruning pyramid coding scheme satisfying all these objectives. Pyramid coding is an approach suitable for progressive image transmission, where the original image is divided into different levels that correspond to successive approximants of the original one. Starting from the original image, a sequence of reduced-size images is formed by averaging intensity values over 2/spl times/2-pixel blocks. This sequence, called the mean pyramid, ends with an image with only one pixel. Then another sequence of images, called the difference pyramid which can be further encoded via vector quantization, is formed by taking the difference of two consecutive images in the mean pyramid. Our quadtree-pruning approach uses only the mean pyramid. Experiments show that the quadtree-pruning pyramid method is quite efficient for lossy compression. Our approach can also be used for lossless compression by simply setting the threshold function to be zero.
{"title":"Progressive image transmission: an adaptive quadtree-pruning approach","authors":"C. Bajaj, Guozhong Zhuang","doi":"10.1109/DCC.1997.582075","DOIUrl":"https://doi.org/10.1109/DCC.1997.582075","url":null,"abstract":"Summary form only given. Progressive, adaptive and hierarchical modes are desirable image coding features. This paper presents a quadtree-pruning pyramid coding scheme satisfying all these objectives. Pyramid coding is an approach suitable for progressive image transmission, where the original image is divided into different levels that correspond to successive approximants of the original one. Starting from the original image, a sequence of reduced-size images is formed by averaging intensity values over 2/spl times/2-pixel blocks. This sequence, called the mean pyramid, ends with an image with only one pixel. Then another sequence of images, called the difference pyramid which can be further encoded via vector quantization, is formed by taking the difference of two consecutive images in the mean pyramid. Our quadtree-pruning approach uses only the mean pyramid. Experiments show that the quadtree-pruning pyramid method is quite efficient for lossy compression. Our approach can also be used for lossless compression by simply setting the threshold function to be zero.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126873208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The method for universal transform coding based on backward adaptation introduced by Goyal et al. (see IEEE Int. Conf. Image Proc., vol.II, p.365-8, 1996) is reviewed and further analyzed. This algorithm uses a linear transform which is periodically updated based on a local Karhunen-Loeve transform (KLT) estimate. The KLT estimate is derived purely from quantized data, so the decoder can track the encoder state without any side information. The effect of estimating only from quantized data is quantitatively analyzed. Two convergence results which hold in the absence of estimation noise are presented. The first applies for any vector dimension but does not preclude the necessity of a sequence of quantization step sizes that goes to zero. The second applies only in the two-dimensional case, but shows local convergence for a fixed, sufficiently small quantization step size. Refinements which reduce the storage and computational requirements of the algorithm are suggested.
{"title":"Universal transform coding based on backward adaptation","authors":"Vivek K Goyal, Jun Zhuang, M. Vetterli","doi":"10.1109/DCC.1997.582046","DOIUrl":"https://doi.org/10.1109/DCC.1997.582046","url":null,"abstract":"The method for universal transform coding based on backward adaptation introduced by Goyal et al. (see IEEE Int. Conf. Image Proc., vol.II, p.365-8, 1996) is reviewed and further analyzed. This algorithm uses a linear transform which is periodically updated based on a local Karhunen-Loeve transform (KLT) estimate. The KLT estimate is derived purely from quantized data, so the decoder can track the encoder state without any side information. The effect of estimating only from quantized data is quantitatively analyzed. Two convergence results which hold in the absence of estimation noise are presented. The first applies for any vector dimension but does not preclude the necessity of a sequence of quantization step sizes that goes to zero. The second applies only in the two-dimensional case, but shows local convergence for a fixed, sufficiently small quantization step size. Refinements which reduce the storage and computational requirements of the algorithm are suggested.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128473848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary form only given. Weighted finite automata (WFA) exploit self-similarities within single images and also video streams to remove spatial and temporal redundancies. The WFA image codec combines techniques from fractal image compression and vector-quantization to achieve performance results for low bit-rates which can be put on a par with state-of-the-art codecs like embedded zerotree wavelet coding. Moreover, frame regeneration of WFA encoded video streams is faster than that of wavelet coded video streams due to the simple mathematical structure of WFA. Therefore, WFA were chosen as a starting point for a fractal-like video compression with hierarchical motion-compensation. Video streams are structured as proposed by the MPEG standards: the entire video is subdivided into several groups of pictures which are made up of one I-frame and a given number of predicted B- or P-frames. The macro block concept of the MPEG standard is replaced by a hierarchical and adaptive image partitioning. We integrated motion compensation with variable block sizes into the WFA coder to exploit the inter-frame redundancy. The general concept of the WFA compression was retained since it already provides a hierarchical subdivision of the image. The video stream is encoded frame by frame with an improved version of the WFA inference algorithm.
{"title":"Video compression with weighted finite automata","authors":"J. Albert, S. Frank, U. Hafner, M. Unger","doi":"10.1109/DCC.1997.582071","DOIUrl":"https://doi.org/10.1109/DCC.1997.582071","url":null,"abstract":"Summary form only given. Weighted finite automata (WFA) exploit self-similarities within single images and also video streams to remove spatial and temporal redundancies. The WFA image codec combines techniques from fractal image compression and vector-quantization to achieve performance results for low bit-rates which can be put on a par with state-of-the-art codecs like embedded zerotree wavelet coding. Moreover, frame regeneration of WFA encoded video streams is faster than that of wavelet coded video streams due to the simple mathematical structure of WFA. Therefore, WFA were chosen as a starting point for a fractal-like video compression with hierarchical motion-compensation. Video streams are structured as proposed by the MPEG standards: the entire video is subdivided into several groups of pictures which are made up of one I-frame and a given number of predicted B- or P-frames. The macro block concept of the MPEG standard is replaced by a hierarchical and adaptive image partitioning. We integrated motion compensation with variable block sizes into the WFA coder to exploit the inter-frame redundancy. The general concept of the WFA compression was retained since it already provides a hierarchical subdivision of the image. The video stream is encoded frame by frame with an improved version of the WFA inference algorithm.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128506193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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