{"title":"On-line decision making for a class of loss functions via Lempel-Ziv parsing","authors":"M. Weinberger, E. Ordentlich","doi":"10.1109/DCC.2000.838156","DOIUrl":"https://doi.org/10.1109/DCC.2000.838156","url":null,"abstract":"Prefetching in computer memory architectures is formalized as a sequential decision problem in which the instantaneous losses depend not only on the current action-observation pair, as in the traditional formulation, but also on past pairs. Motivated by the prefetching application, we study a class of loss functions that admit an efficient on-line decision algorithm. The algorithm uses the LZ78 parsing rule to dynamically build a tree, different from the classical LZ78 tree, and makes decisions based on the current node in a traversal path, determined by the sequence of observations. The asymptotic performance is essentially as good as that of the best finite-state strategy determined in hindsight, with full knowledge of the given sequence of observations. The related notion of delayed FS predictability is introduced, and its properties are studied.","PeriodicalId":218804,"journal":{"name":"Proceedings DCC 2000. Data Compression Conference","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115248049","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}
Adam H. Li, S. Kittitornkun, Y. Hu, Dong-Seek Park, J. Villasenor
{"title":"Data partitioning and reversible variable length codes for robust video communications","authors":"Adam H. Li, S. Kittitornkun, Y. Hu, Dong-Seek Park, J. Villasenor","doi":"10.1109/DCC.2000.838186","DOIUrl":"https://doi.org/10.1109/DCC.2000.838186","url":null,"abstract":"Low bit-rate multimedia communication over wireless channels has received much attention recently. A key challenge in low bit-rate wireless communication is the very high error rate during transmission. This demands error resilient services that exhibit graceful performance degradation while operating in a highly noisy channel. Among video, audio and other multimedia communication modalities, we focus on the error-resilient transmission of video signals over wireless channels in this paper. Specifically, efforts in developing H.263++ Annex V error resilient data partitioning with reversible variable length code (RVLC) are described in detail. The performance over error-prone channels is analyzed and the effectiveness of the syntax is demonstrated with extensive simulations.","PeriodicalId":218804,"journal":{"name":"Proceedings DCC 2000. Data Compression Conference","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116830012","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}
{"title":"Text compression based on variable-to-fixed codes for Markov sources","authors":"I. Tabus, G. Korodi, J. Rissanen","doi":"10.1109/DCC.2000.838153","DOIUrl":"https://doi.org/10.1109/DCC.2000.838153","url":null,"abstract":"An n-state Markov model for symbol occurrences is extended to an equivalent source for variable length strings of symbols in a dictionary at every state i, which are to be encoded with the string index in the dictionary. The algorithm for building the n dictionaries optimizes the rate subject to a given total number of entries in the dictionaries, and it is practical even for Markov sources with thousands of states. The speed of the algorithm stems from encoding by table look-ups of the strings instead of single symbols. For this the n dictionaries need be known both to the encoder and the decoder. A static version of the algorithm is very well suited for creation of compressed files with random access. An adaptive version is shown to be faster than the methods in the PPM class, while providing only slightly lower compression ratios.","PeriodicalId":218804,"journal":{"name":"Proceedings DCC 2000. Data Compression Conference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123008681","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}
{"title":"What's your sign?: efficient sign coding for embedded wavelet image coding","authors":"A. Deever, S. Hemami","doi":"10.1109/DCC.2000.838167","DOIUrl":"https://doi.org/10.1109/DCC.2000.838167","url":null,"abstract":"Wavelet transform coefficients are defined by both a magnitude and a sign. While promising algorithms exist for efficiently coding the transform coefficient magnitudes, current wavelet image coding algorithms are not efficient at coding the sign of the transform coefficients. It is generally assumed that there is no compression gain to be obtained from entropy coding of the sign. Only recently have some authors begun to investigate this component of wavelet image coding. In this paper, sign coding is examined in detail in the context of an embedded wavelet image coder. It is shown that PSNR improvements of up to 0.7 dB are possible from an efficient modeling and entropy coding of the coefficient signs, combined with a new extrapolation technique, which is used to improve the final estimate of insignificant coefficients. These sign coding techniques are applicable to any genre (e.g., zero-tree, context-model) of the embedded wavelet image coder.","PeriodicalId":218804,"journal":{"name":"Proceedings DCC 2000. Data Compression Conference","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126269100","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}
{"title":"Distributed Internet-adaptive image compression","authors":"Kunal Mukherjee, A. Mukherjee, T. Acharya","doi":"10.1109/DCC.2000.838210","DOIUrl":"https://doi.org/10.1109/DCC.2000.838210","url":null,"abstract":"Summary form only given. The contribution of this paper is an extension of the pruned tree-structured hierarchical vector quantization (PTSHVQ) algorithm, with a focus on designing and benchmarking a complete Internet-aware system for the real-time delivery of compressed images. We extend the PTSHVQ algorithm to enable the encoder to use a combination of \"coarse\" and \"fine\" rate control mechanisms. The coarse rate control is achieved by switching between successive HVQ lookup tables, thereby changing the bit rate on a logarithmic scale, e.g., to give 4, 2, 1, 0.5 bpp, etc. The fine rate control is achieved by sub-setting into the address space of the current HVQ lookup table, e.g., generating 4, 3.5, 3, 2.5 bpp. The combination of these two strategies of switching between HVQ stages and subsetting the available range of addresses at each stage gives us a much larger dynamic range as well as finer calibration of operational bit rates than any existing HVQ system. Organizing the codebooks into binary tree structures by using the greedy BFOS algorithm guarantees locally optimal rate-distortion behavior at each operational bit rate. The function of the distributed transcoder is to enable the regulation of operational bandwidth over a heterogeneous network, thereby guaranteeing fairness in broadcast type applications.","PeriodicalId":218804,"journal":{"name":"Proceedings DCC 2000. Data Compression Conference","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134450092","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}
{"title":"Piecewise linear image coding using surface triangulation and geometric compression","authors":"Tianyu Lu, Zisheng Le, D. Yun","doi":"10.1109/DCC.2000.838181","DOIUrl":"https://doi.org/10.1109/DCC.2000.838181","url":null,"abstract":"The provably NP-hard problem of finding optimal piecewise linear approximation for images is extended from 1D curve fitting to 2D surface fitting by a dual-agent algorithm. The results not only yield a storage-efficient codec for range, or intensity, images but also a surface triangulation technique to generate a succinct, accurate and visually pleasant 3D visualization model. Compared with the traditional piecewise linear image coding (PLIC) algorithms, triangulation of a range image is more adaptive due to conformity of the shape, orientation and size of triangles with the image contents. The triangularization algorithm presented here differs from previous approaches in that it strives to minimize the total number of triangles (or vertices) needed to approximate the image surface while keeping the deviation of any intensity value to within a prescribed error tolerance. Unlike most methods of bottom-up triangularization, which could bog down before any mesh simplification even begins, this algorithm fits the surface in a top-down manner, avoiding the generation of most unnecessary triangles. When combined with an efficient 3D triangulation-encoding scheme, the algorithm achieves compact code length with guaranteed error bound, thus providing a more faithful representation of all image features. A variety of benchmark test images have been experimented with and compared.","PeriodicalId":218804,"journal":{"name":"Proceedings DCC 2000. Data Compression Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130286226","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}
{"title":"Compression of Lumigraph with multiple reference frame (MRF) prediction and just-in-time rendering","authors":"Cha Zhang, Jin Li","doi":"10.1109/DCC.2000.838165","DOIUrl":"https://doi.org/10.1109/DCC.2000.838165","url":null,"abstract":"In the form of a 2D image array, Lumigraph captures the complete appearance of an object or a scene, and is able to quickly render a novel view independent of the scene/object complexity. Since the data amount of Lumigraph is huge, efficient storage and access for Lumigraph are essential. In this paper, we propose a multiple reference frame (MRF) structure to compress the Lumigraph data. By predicting the Lumigraph view from multiple neighbor views, a higher compression ratio is achieved. We also implement the key functionality of just-in-time (JIT) Lumigraph rendering, in which only a small portion of the compressed bitstream necessary for rendering the current view is accessed and decoded. JIT rendering eliminates the need to predecode the entire Lumigraph data set, thus greatly reduces the memory requirement of Lumigraph rendering. A decoder cache has been implemented to speed up rendering by reusing the decoded data. The trade off between the computational speed and cache size of the decoder is discussed in the paper.","PeriodicalId":218804,"journal":{"name":"Proceedings DCC 2000. Data Compression Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132105509","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}
{"title":"Tree-based search for ECVQ","authors":"J. Cardinal","doi":"10.1109/DCC.2000.838195","DOIUrl":"https://doi.org/10.1109/DCC.2000.838195","url":null,"abstract":"Summary form only given. We propose two new tree-based search algorithms for vector quantizers using an additive weighted distance measure, such as ECVQ (entropy constrained vector quantization) (Chou et al., 1989). Both algorithms are based on a recursive space division technique, and use a bounding object at each node of the tree, in order to quickly eliminate subsets of the codebook during the search. The structure is more general than the k-d tree and the algorithm performs an optimal search similar to the one analyzed by Berchtold et al. (1997). We prove a theorem that defines the necessary and sufficient condition for any set of points to be a valid bounding object, i.e. to define a lossless pruning rule for the additive weighed Euclidean distance. The first algorithm presented uses rectangles as bounding objects, and the other uses spheres. We experimentally compare our approach with another recent one (Johnson et al., 1996), and show that the new algorithm using bounding rectangles performs significantly better for medium and high bitrate coding (>0.1 bits/sample) of a Gaussian process. This algorithm uses approximately 29 times less multiplications than a full codebook search at 1 bits/sample.","PeriodicalId":218804,"journal":{"name":"Proceedings DCC 2000. Data Compression Conference","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130749101","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}
{"title":"Design of asymmetric multiple description lattice vector quantizers","authors":"S. Diggavi, N. Sloane, V. Vaishampayan","doi":"10.1109/DCC.2000.838189","DOIUrl":"https://doi.org/10.1109/DCC.2000.838189","url":null,"abstract":"We consider the design of asymmetric multiple description lattice quantizers that cover the entire spectrum of the distortion profile, ranging from symmetric or balanced to successively refinable. We present a solution to a labeling problem, which is an important part of the construction, along with a general design procedure. This procedure is illustrated using a ZZ/sup 2/ lattice. We also evaluate its rate-distortion performance and compare it to known information theoretic bounds.","PeriodicalId":218804,"journal":{"name":"Proceedings DCC 2000. Data Compression Conference","volume":"55 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116571051","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}
{"title":"Lossless and lossy broadcast system source codes: theoretical limits, optimal design, and empirical performance","authors":"Qian Zhao, M. Effros","doi":"10.1109/DCC.2000.838146","DOIUrl":"https://doi.org/10.1109/DCC.2000.838146","url":null,"abstract":"Broadcast systems are a class of networks where one system node (transmitter) simultaneously sends both common and independent, information to multiple nodes (receivers) in the system. Compressing the messages transmitted in such systems using traditional (single-transmitter, single-receiver) techniques requires use of a collection of independent source codes, one for each message sent through the system. The result of this approach is a system with multiple independent encoders at the transmitter and multiple independent decoders at each receiver. An alternative approach is to design a single joint encoder at the transmitter and a single joint decoder at each receiver. We call the resulting code a \"broadcast system source code\". This paper treats the theory and practice of optimal lossless and lossy (fixed- and variable-rate) broadcast system source codes. The results given include: theoretical limits for lossless source code performance on broadcast systems; an optimal lossless source code design algorithm; an optimal lossy source code design algorithm that generalizes the generalized Lloyd algorithm to broadcast systems; and experimental results for fixed- and variable-rate code performance in a two-receiver broadcast system.","PeriodicalId":218804,"journal":{"name":"Proceedings DCC 2000. Data Compression Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128924836","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}