Summary form only given. Multiview stereo imaging uses arrays of cameras to capture scenes from multiple perspectives. This form of imagery is used in systems that allow the user to survey the scene, for example by head motion. Very little work has been reported on compression schemes for multiview images. Multiview image sets tend to be very large because they may contain several hundred views, but there is considerable redundancy among the views which makes them highly compressible. This paper compares methods for compressing large multiview stereo image sets. There is an obvious similarity between multiview image sets and video sequences. As a baseline we compressed a set of multiview stereo images with JPEG on each image individually and MPEG-1 applied to the whole set. The average bits per pixel were reduced by roughly a factor of two over individual frame compression, at constant mean square error (MSE). Stereo specific perceptual distortions can be viewed in anaglyph representations of the data set. Another method, unique to this data type, is based on residual coding with respect to a synthetic "panoramic still" containing information from all of the images in the set. In this method we synthesize a single panoramic image from all of the members of a registered set, code the panoramic image, and then code the residual images formed by subtracting the individual images from the corresponding position on the panorama. Initial results with this method appear to give a similar MSE rate distortion curve as the MPEG based techniques. However, the panoramic still method is inherently random access.
{"title":"Compression comparisons for multiview stereo","authors":"D.K. Jones, M.W. Maier","doi":"10.1109/DCC.1997.582103","DOIUrl":"https://doi.org/10.1109/DCC.1997.582103","url":null,"abstract":"Summary form only given. Multiview stereo imaging uses arrays of cameras to capture scenes from multiple perspectives. This form of imagery is used in systems that allow the user to survey the scene, for example by head motion. Very little work has been reported on compression schemes for multiview images. Multiview image sets tend to be very large because they may contain several hundred views, but there is considerable redundancy among the views which makes them highly compressible. This paper compares methods for compressing large multiview stereo image sets. There is an obvious similarity between multiview image sets and video sequences. As a baseline we compressed a set of multiview stereo images with JPEG on each image individually and MPEG-1 applied to the whole set. The average bits per pixel were reduced by roughly a factor of two over individual frame compression, at constant mean square error (MSE). Stereo specific perceptual distortions can be viewed in anaglyph representations of the data set. Another method, unique to this data type, is based on residual coding with respect to a synthetic \"panoramic still\" containing information from all of the images in the set. In this method we synthesize a single panoramic image from all of the members of a registered set, code the panoramic image, and then code the residual images formed by subtracting the individual images from the corresponding position on the panorama. Initial results with this method appear to give a similar MSE rate distortion curve as the MPEG based techniques. However, the panoramic still method is inherently random access.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"25 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":"126516205","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. This paper proposes several enhancements to the AC prediction approach, adapted by the Joint Photographic Expert Group (JPEG), for reduction of the blocking artifact effects. Our decoder uses value of reconstructed pixels of the already decoded part of the image, instead of the their DCT components. The major contribution of the paper is that we divide the prediction of DCT coefficients in two parts. For the low frequency coefficients, we solve a minimization problem. Its objective is to reduce the block boundary edge variance (BEV). The problem is solved analytically and its solution predicts DCT coefficients of a block in the terms of the first four coefficients of the four adjacent blocks. In this process, we also determine an optimal solution to the minimization of the mean squared difference of slopes (MSDS) considered for the same problem and computed using a quadratic programming method. For the mid-range frequency coefficients, we follow the interpolation method and interpolate image segments by ternary polynomials (JPEG uses quadratic polynomials). The smallest possible 9/spl times/9 pixel image segments are considered for the prediction of coefficients of 8/spl times/8 blocks (JPEG considers 24/spl times/24 pixel segments). The paper presents a complete formulation of the prediction equations, not provided by the JPEG. The paper also proposes three new statistical criterion to measure block boundary discontinuities. All enhancements have been added to a JPEG software. Results of several experiments using this software are given to compare the performance of different implementations of the AC prediction approach.
{"title":"Enhancements to the JPEG implementation of block smoothing method","authors":"G. Lakhani","doi":"10.1109/DCC.1997.582108","DOIUrl":"https://doi.org/10.1109/DCC.1997.582108","url":null,"abstract":"Summary form only given. This paper proposes several enhancements to the AC prediction approach, adapted by the Joint Photographic Expert Group (JPEG), for reduction of the blocking artifact effects. Our decoder uses value of reconstructed pixels of the already decoded part of the image, instead of the their DCT components. The major contribution of the paper is that we divide the prediction of DCT coefficients in two parts. For the low frequency coefficients, we solve a minimization problem. Its objective is to reduce the block boundary edge variance (BEV). The problem is solved analytically and its solution predicts DCT coefficients of a block in the terms of the first four coefficients of the four adjacent blocks. In this process, we also determine an optimal solution to the minimization of the mean squared difference of slopes (MSDS) considered for the same problem and computed using a quadratic programming method. For the mid-range frequency coefficients, we follow the interpolation method and interpolate image segments by ternary polynomials (JPEG uses quadratic polynomials). The smallest possible 9/spl times/9 pixel image segments are considered for the prediction of coefficients of 8/spl times/8 blocks (JPEG considers 24/spl times/24 pixel segments). The paper presents a complete formulation of the prediction equations, not provided by the JPEG. The paper also proposes three new statistical criterion to measure block boundary discontinuities. All enhancements have been added to a JPEG software. Results of several experiments using this software are given to compare the performance of different implementations of the AC prediction approach.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"49 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":"132678230","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. The output of medical imaging devices is increasingly digital and both storage space and transmission time of the images profit from compression. The introduction of PACS systems into the hospital environment fortifies this need. Since any loss of diagnostic information is to be avoided, lossless compression techniques are preferable. We present an experimental comparison of several lossless coders and investigate their compression efficiency and speed for different types of medical images. The coders are: five image coders (LJPEG, BTPC, FELICS, S+P, CALIC), and two general-purpose coders (GnuZIP, STAT). The medical imaging techniques are: CT, MRI, X-ray, angiography, mammography, PET and echography. Lossless JPEG (LJPEG), the current lossless compression standard, combines simple linear prediction with Huffman coding. Binary tree predictive coding (BTPC) is a multi-resolution technique which decomposes the image into a binary tree. The fast and efficient lossless image compression system (FELICS) conditions the pixel data on the values of the two nearest neighbours. Compression with reversible embedded wavelets (S+P) uses a lossless wavelet transform. The context-based, adaptive, lossless/nearly-lossless coding scheme for continuous-tone images (CALIC) combines non-linear prediction with advanced statistical error modelling techniques. GnuZIP uses LZ77, a form of sliding window compression. STAT is a PPM-lilte general-purpose compression technique. We give combined compression ratio vs. speed results for the different compression methods as an average over the different image types.
{"title":"An experimental comparison of several lossless image coders for medical images","authors":"K. Denecker, J. Van Overloop, I. Lemahieu","doi":"10.1109/DCC.1997.582091","DOIUrl":"https://doi.org/10.1109/DCC.1997.582091","url":null,"abstract":"Summary form only given. The output of medical imaging devices is increasingly digital and both storage space and transmission time of the images profit from compression. The introduction of PACS systems into the hospital environment fortifies this need. Since any loss of diagnostic information is to be avoided, lossless compression techniques are preferable. We present an experimental comparison of several lossless coders and investigate their compression efficiency and speed for different types of medical images. The coders are: five image coders (LJPEG, BTPC, FELICS, S+P, CALIC), and two general-purpose coders (GnuZIP, STAT). The medical imaging techniques are: CT, MRI, X-ray, angiography, mammography, PET and echography. Lossless JPEG (LJPEG), the current lossless compression standard, combines simple linear prediction with Huffman coding. Binary tree predictive coding (BTPC) is a multi-resolution technique which decomposes the image into a binary tree. The fast and efficient lossless image compression system (FELICS) conditions the pixel data on the values of the two nearest neighbours. Compression with reversible embedded wavelets (S+P) uses a lossless wavelet transform. The context-based, adaptive, lossless/nearly-lossless coding scheme for continuous-tone images (CALIC) combines non-linear prediction with advanced statistical error modelling techniques. GnuZIP uses LZ77, a form of sliding window compression. STAT is a PPM-lilte general-purpose compression technique. We give combined compression ratio vs. speed results for the different compression methods as an average over the different image types.","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":"131201565","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. We present improvements to a general type of lossless, lossy, and refinement coding of bi-level images (Martins and Forchhammer, 1996). Loss is introduced by flipping pixels. The pixels are coded using arithmetic coding of conditional probabilities obtained using a template as is known from JBIG and proposed in JBIG-2 (Martins and Forchhammer). Our new state-of-the-art results are obtained using the more general free tree instead of a template. Also we introduce multiple refinement template coding. The lossy algorithm is analogous to the greedy 'rate-distortion'-algorithm of Martins and Forchhammer but is based on the free tree.
{"title":"Lossy/lossless coding of bi-level images","authors":"Bo Martins, Soren Forchhammer","doi":"10.1109/DCC.1997.582116","DOIUrl":"https://doi.org/10.1109/DCC.1997.582116","url":null,"abstract":"Summary form only given. We present improvements to a general type of lossless, lossy, and refinement coding of bi-level images (Martins and Forchhammer, 1996). Loss is introduced by flipping pixels. The pixels are coded using arithmetic coding of conditional probabilities obtained using a template as is known from JBIG and proposed in JBIG-2 (Martins and Forchhammer). Our new state-of-the-art results are obtained using the more general free tree instead of a template. Also we introduce multiple refinement template coding. The lossy algorithm is analogous to the greedy 'rate-distortion'-algorithm of Martins and Forchhammer but is based on the free tree.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"22 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":"126603806","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. Because of stringent bandwidth requirement, very low bitrate video coding usually uses lower frame rates in order to comply with the bitrate constraint. With a reasonably low frame rate, it can reserve basic visual information of an image sequence. However, on special occasions or for specific human understanding purposes, it can barely provide enough temporal resolution. In these cases, we would apply content-based temporal scalability to enhance temporal resolution for desired objects/areas in an image, with a reasonable increase of bitrate. We propose a motion-adapted encoding scheme for content-based temporal scalability in very low bitrate video coding. This coding scheme selectively encodes desired objects and makes proper adjustment to the rest of the scene. Content-based scalability and temporal scalability are achieved via two separate coding steps. This coding scheme is efficient for image sequences with hierarchical structure, such as sequences with background motion and sequences with single moving object. Simulations are done on videotelephony sequences and selected MPEG4 test sequences.
{"title":"Motion-adapted content-based temporal scalability in very low bitrate video coding","authors":"C. Chu, D. Anastassiou, Shih-Fu Chang","doi":"10.1109/DCC.1997.582087","DOIUrl":"https://doi.org/10.1109/DCC.1997.582087","url":null,"abstract":"Summary form only given. Because of stringent bandwidth requirement, very low bitrate video coding usually uses lower frame rates in order to comply with the bitrate constraint. With a reasonably low frame rate, it can reserve basic visual information of an image sequence. However, on special occasions or for specific human understanding purposes, it can barely provide enough temporal resolution. In these cases, we would apply content-based temporal scalability to enhance temporal resolution for desired objects/areas in an image, with a reasonable increase of bitrate. We propose a motion-adapted encoding scheme for content-based temporal scalability in very low bitrate video coding. This coding scheme selectively encodes desired objects and makes proper adjustment to the rest of the scene. Content-based scalability and temporal scalability are achieved via two separate coding steps. This coding scheme is efficient for image sequences with hierarchical structure, such as sequences with background motion and sequences with single moving object. Simulations are done on videotelephony sequences and selected MPEG4 test sequences.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"5 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":"130737212","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. It is well-known that tree-structured vector quantization may sacrifice performance for reduced computation. The performance loss can be attributed to two separate sources, the design approximation and the search inaccuracy. To measure the search performance, we define the search accuracy as the percentage of input vectors that are quantized with minimum distortion. Our studies show that low search accuracy is the main cause of performance loss for some of the best current tree-structured vector quantizers. Although the design approximation and search performance can be analyzed separately, we observe that the result of design may actually affect the search accuracy. Most of the current design techniques seek to minimize the distortion in the design without any consideration of their effect on the search. The tree search accuracy as a result of these designs could be as low as 50 percent. In order to improve the overall performance, the tree design should not be optimized without consideration of tree search accuracy. The difficulty is that it is not possible to measure the search accuracy at the design stage. We develop a design algorithm that incorporates the search accuracy and produce a tree-structured that improves the search accuracy significantly. Experimental results in image compression show that the strategy works surprisingly well in improving the tree search accuracy from a low of 50% to over 80 and 90%.
{"title":"The search accuracy of tree-structured VQ","authors":"J. Lin","doi":"10.1109/DCC.1997.582112","DOIUrl":"https://doi.org/10.1109/DCC.1997.582112","url":null,"abstract":"Summary form only given. It is well-known that tree-structured vector quantization may sacrifice performance for reduced computation. The performance loss can be attributed to two separate sources, the design approximation and the search inaccuracy. To measure the search performance, we define the search accuracy as the percentage of input vectors that are quantized with minimum distortion. Our studies show that low search accuracy is the main cause of performance loss for some of the best current tree-structured vector quantizers. Although the design approximation and search performance can be analyzed separately, we observe that the result of design may actually affect the search accuracy. Most of the current design techniques seek to minimize the distortion in the design without any consideration of their effect on the search. The tree search accuracy as a result of these designs could be as low as 50 percent. In order to improve the overall performance, the tree design should not be optimized without consideration of tree search accuracy. The difficulty is that it is not possible to measure the search accuracy at the design stage. We develop a design algorithm that incorporates the search accuracy and produce a tree-structured that improves the search accuracy significantly. Experimental results in image compression show that the strategy works surprisingly well in improving the tree search accuracy from a low of 50% to over 80 and 90%.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"113 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":"117284377","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. The Japanese language has several thousand distinct characters, and the character code length is 16 bits. In such documents the 16-bit units are interrelated. Conventional text compression employs 8-bit sampling because the compressed object is usually English text. We investigated compression schemes based on 16-bit sampling, expecting it to improve the compression performance. In Japanese text where words are short, statistical schemes with a PPM provide better compression ratios than slide dictionary schemes. So we investigated the 16-bit sampling based on statistical schemes with a PPM model. We show the 16-bit sampling scheme provides good compression ratios in short documents under several tens of kilobytes, such as office reports. The processing speed is also better.
{"title":"Study of Japanese text compression","authors":"N. Satoh, T. Morihara, Y. Okada, S. Yoshida","doi":"10.1109/DCC.1997.582134","DOIUrl":"https://doi.org/10.1109/DCC.1997.582134","url":null,"abstract":"Summary form only given. The Japanese language has several thousand distinct characters, and the character code length is 16 bits. In such documents the 16-bit units are interrelated. Conventional text compression employs 8-bit sampling because the compressed object is usually English text. We investigated compression schemes based on 16-bit sampling, expecting it to improve the compression performance. In Japanese text where words are short, statistical schemes with a PPM provide better compression ratios than slide dictionary schemes. So we investigated the 16-bit sampling based on statistical schemes with a PPM model. We show the 16-bit sampling scheme provides good compression ratios in short documents under several tens of kilobytes, such as office reports. The processing speed is also better.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"186 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120873584","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. Transform coding has become the de facto standard for image and video compression. The design of adaptive signal transforms for image coding usually follows one of the two approaches: adaptive tree/quantizer design with fixed subband filter banks and adaptive subband filter bank design with fixed quantizers and tree topology. The main objective of our work is to integrate these two paradigms in an image coder in which subband filter banks, tree structures and quantizers are all adapted. We design a codebook for the filters, tree and quantizers. The codebook design algorithm uses a training set made of images that are assumed to be representative of the broad class of images of interest. We first design the filters and then the quantizers. In the filter design phase, we visit nodes in a top-down fashion and design a filter codebook for each tree node. The optimal filter codebook for each node is designed so as to minimize the theoretical coding gain-based rate. The design of the quantizers and the weights for the splitting decisions is done jointly using a greedy iterative algorithm based on the single tree algorithm of Ramchandran et al. (1993). The actual coding algorithm finds, based on the codebook design, the optimized filter banks, tree structure, and quantizer choices for each node of the tree. In our experimental setup we used a training set of 20 images representative of four image classes.
只提供摘要形式。变换编码已经成为图像和视频压缩的事实上的标准。用于图像编码的自适应信号变换的设计通常遵循两种方法中的一种:具有固定子带滤波器组的自适应树/量化器设计和具有固定量化器和树拓扑的自适应子带滤波器组设计。我们工作的主要目标是将这两种范式集成到图像编码器中，其中子带滤波器组，树结构和量化器都适用。我们为滤波器、树和量化器设计了一个码本。码本设计算法使用一个由图像组成的训练集，这些图像被认为是感兴趣的大类图像的代表。我们首先设计滤波器，然后是量化器。在过滤器设计阶段，我们以自顶向下的方式访问节点，并为每个树节点设计一个过滤器代码本。设计了每个节点的最优滤波器码本，使理论编码增益率最小。在Ramchandran et al.(1993)的单树算法的基础上，采用贪心迭代算法联合设计分拆决策的量化器和权值。实际的编码算法根据码本设计，为树的每个节点找到优化的滤波器组、树结构和量化器选择。在我们的实验设置中，我们使用了一个由代表四个图像类别的20个图像组成的训练集。
{"title":"\"Universal\" transform image coding based on joint adaptation of filter banks, tree structures and quantizers","authors":"V. Pavlovic, K. Ramchandran, P. Moulin","doi":"10.1109/DCC.1997.582130","DOIUrl":"https://doi.org/10.1109/DCC.1997.582130","url":null,"abstract":"Summary form only given. Transform coding has become the de facto standard for image and video compression. The design of adaptive signal transforms for image coding usually follows one of the two approaches: adaptive tree/quantizer design with fixed subband filter banks and adaptive subband filter bank design with fixed quantizers and tree topology. The main objective of our work is to integrate these two paradigms in an image coder in which subband filter banks, tree structures and quantizers are all adapted. We design a codebook for the filters, tree and quantizers. The codebook design algorithm uses a training set made of images that are assumed to be representative of the broad class of images of interest. We first design the filters and then the quantizers. In the filter design phase, we visit nodes in a top-down fashion and design a filter codebook for each tree node. The optimal filter codebook for each node is designed so as to minimize the theoretical coding gain-based rate. The design of the quantizers and the weights for the splitting decisions is done jointly using a greedy iterative algorithm based on the single tree algorithm of Ramchandran et al. (1993). The actual coding algorithm finds, based on the codebook design, the optimized filter banks, tree structure, and quantizer choices for each node of the tree. In our experimental setup we used a training set of 20 images representative of four image classes.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"96 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132708190","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, as follows. We initiate a study of parsing-based compression algorithms such as LZ77 and LZ78 by considering the empirical entropy of the input string. For any string s, we define the k-th order entropy H/sub k/(s) by looking at the number of occurrences of each symbol following each k-length substring inside s. The value H/sub k/(s) is a lower bound to the compression ratio of a statistical modeling algorithm which predicts the probability of the next symbol by looking at the k most recently seen characters. Therefore, our analysis provides a means for comparing Lempel-Ziv methods with the more powerful, but slower, PPM algorithms. Our main contribution is a comparison of the compression ratio of Lempel-Ziv algorithms with the zeroth order entropy H/sub 0/. First we show that for low entropy strings LZ78 compression ratio can be much higher than H/sub 0/. Then, we present a modified algorithm which combines LZ78 with run length encoding and is able to compress efficiently also low entropy strings.
{"title":"Some entropic bounds for Lempel-Ziv algorithms","authors":"S. Rao Kosaraju, G. Manzini","doi":"10.1109/DCC.1997.582106","DOIUrl":"https://doi.org/10.1109/DCC.1997.582106","url":null,"abstract":"Summary form only given, as follows. We initiate a study of parsing-based compression algorithms such as LZ77 and LZ78 by considering the empirical entropy of the input string. For any string s, we define the k-th order entropy H/sub k/(s) by looking at the number of occurrences of each symbol following each k-length substring inside s. The value H/sub k/(s) is a lower bound to the compression ratio of a statistical modeling algorithm which predicts the probability of the next symbol by looking at the k most recently seen characters. Therefore, our analysis provides a means for comparing Lempel-Ziv methods with the more powerful, but slower, PPM algorithms. Our main contribution is a comparison of the compression ratio of Lempel-Ziv algorithms with the zeroth order entropy H/sub 0/. First we show that for low entropy strings LZ78 compression ratio can be much higher than H/sub 0/. Then, we present a modified algorithm which combines LZ78 with run length encoding and is able to compress efficiently also low entropy strings.","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":"129173042","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. A real-time, low bit rate, intraframe video codec that is robust to packet erasure is developed for coding and QCIF gray scale video sequences. The system combines subband image coding, entropy coded scalar quantization, subband trees of wavelet coefficients, runlength coding, and Huffman coding. The encoded bit stream is encapsulated in independent variable-length packets. Isolation of spatially related trees of subband coefficients makes the system robust to packet erasure. The design objectives are to use a small encoding rate, recover gracefully from erasures in which packets of data have been erased, and have a software-only implementation that runs in real-time. The packet loss rate can be as large as 30%. We investigate several passive methods of recovering from packet erasures, including (i) replace missing pixels by the subband mean, (ii) replace missing pixels with the most recently received subband pixels in previous frames, (iii) explicit error control coding of the lower frequency subbands, and (iv) the use of interpolation and a one-frame delay to estimate the erased subband pixels.
{"title":"Intraframe low bit rate video coding robust to packet erasure","authors":"V.J. Crump, T. Fischer","doi":"10.1109/DCC.1997.582088","DOIUrl":"https://doi.org/10.1109/DCC.1997.582088","url":null,"abstract":"Summary form only given. A real-time, low bit rate, intraframe video codec that is robust to packet erasure is developed for coding and QCIF gray scale video sequences. The system combines subband image coding, entropy coded scalar quantization, subband trees of wavelet coefficients, runlength coding, and Huffman coding. The encoded bit stream is encapsulated in independent variable-length packets. Isolation of spatially related trees of subband coefficients makes the system robust to packet erasure. The design objectives are to use a small encoding rate, recover gracefully from erasures in which packets of data have been erased, and have a software-only implementation that runs in real-time. The packet loss rate can be as large as 30%. We investigate several passive methods of recovering from packet erasures, including (i) replace missing pixels by the subband mean, (ii) replace missing pixels with the most recently received subband pixels in previous frames, (iii) explicit error control coding of the lower frequency subbands, and (iv) the use of interpolation and a one-frame delay to estimate the erased subband pixels.","PeriodicalId":403990,"journal":{"name":"Proceedings DCC '97. Data Compression Conference","volume":"21 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":"116941484","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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