2007 Data Compression Conference (DCC'07)最新文献

{"title":"An Efficient Algorithm For The Inverse ST Problem","authors":"Ge Nong, Sen Zhang","doi":"10.1109/DCC.2007.11","DOIUrl":"https://doi.org/10.1109/DCC.2007.11","url":null,"abstract":"Summary form given only. The Schindler transform (ST) can speed up the block sorting phase of the Burrows-Wheeler transform (BWT) by limiting the context sorting to the first k (k E [0, N], where N is the length of the text) positions only. Under the ST's partial sorting scheme, if two rows share the exactly same k-order context, they may not be ordered alphabetically; instead, the relative order between them in the original matrix is preserved in the transformed matrix. A major tradeoff for the ST to achieve the speedup gain over the BWT is that the inverse ST appears to be more complicated than the inverse BWT. To deal with the existence of identical k-order contexts, Schindler suggested a hash based approach in which the text retrieval has to rely on the hash table based context lookup, which in turn has to rely on the complete retrieval of all the k-order contexts. An improved solution proposed by Yokoo uses no hash table; however, it still needs to restore all the k-order contexts, which clearly requires O(kN) for both the time and the space complexities. Recently, Nong and Zhang had proposed an auxiliary vector based framework, which is different from any possible k-order context retrieval based approaches formerly suggested by others, but similar to that used for the inverse BWT. This framework relies on two size-N vectors Tk and Ck (details are omitted due to space limit) to correctly retrieve the true immediate preceding character for a given character, which allows the original text to be recovered directly from the transformed text without statically restoring the complete fc-order contexts. As a consequence, this framework requires only O(N) space. However, its running time remains to be O(kN), for it has to visit each column of the fc-order context matrix to obtain Tk and Ck. Since Tk and Ck can be deduced from the context switch vector D (the data structure indicating whether each pair of two neighbor rows in the context matrix are the same or not) in linear time, the more efficient the D can be calculated, the faster the ST can be inverted. If two k-order contexts are different, either their first halves are different already, or their second halves are different; furthermore, the second half of the k-order context matrix can be deduced from its first half due to the rotating scheme in ST. Based on this observation, we proposed a dynamic programming approach to quickly calculate D by doubling the steps to reach the fcth column in comparing the fc-order contexts. This \"doubling technique\" based algorithm requires only O(Nlogk) time to calculate D, thus resulting in an O(N log k) time complexity algorithm to invert ST. The space complexity of the algorithm remains to be O(N). This new algorithm can be used to build efficient compression solutions based on the ST.","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125419478","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 as Data Transformation","authors":"Kiem-Phong Vo","doi":"10.1109/DCC.2007.23","DOIUrl":"https://doi.org/10.1109/DCC.2007.23","url":null,"abstract":"Summary form only given. Conventional compression techniques exploit general redundancy features in data to compress them. For example, Huffman or Lempel-Ziv techniques compresses data by statistical modeling or string matching while the Burrows-Wheeler Transform simply sorts data by context to improve compressibility. On the other hand, data can often be compressed better by exploiting their specific features. For example, columns or fields in a database table tend to be sparse, but not rows. Techniques have been developed to either group related table columns or compute dependency among them to transform data and enhance compressibility. The Vcodex data transformation platform provides a framework to develop and use such data transforms. That is, it treats compression techniques as invertible data transforms that can be composed together for specific tasks. In this way, data transformation remains general and can include techniques for encryption and others.","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134507471","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 Grayscale Stereo Image Coding with Unsupervised Learning of Disparity","authors":"D. Varodayan, Aditya Mavlankar, M. Flierl, B. Girod","doi":"10.1109/DCC.2007.35","DOIUrl":"https://doi.org/10.1109/DCC.2007.35","url":null,"abstract":"Distributed compression is particularly attractive for stereo images since it avoids communication between cameras. Since compression performance depends on exploiting the redundancy between images, knowing the disparity is important at the decoder. Unfortunately, distributed encoders cannot calculate this disparity and communicate it. We consider the compression of grayscale stereo images, and develop an expectation maximization algorithm to perform unsupervised learning of disparity during the decoding procedure. Towards this, we devise a novel method for joint bitplane distributed source coding of grayscale images. Our experiments with both natural and synthetic 8-bit images show that the unsupervised disparity learning algorithm outperforms a system which does no disparity compensation by between 1 and more than 3 bits/pixel and performs nearly as well as a system which knows the disparity through an oracle","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129480347","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":"Guided Quaternary Reaching Method for Wavelet-based Image Compression","authors":"Xiteng Liu","doi":"10.1109/DCC.2007.42","DOIUrl":"https://doi.org/10.1109/DCC.2007.42","url":null,"abstract":"Summary form only given. In this paper, a simple - easy to understand, easy to implement - high performance wavelet-based image compression algorithm using guided quaternary reaching (GQR) method is proposed. Compared with JPEG2000, GQR algorithm has two remarkable merits: 1). much lower algorithmic complexity and 2). higher compression efficiency In a wavelet-based image compression system, wavelet coefficients are rearranged into hierarchical subbands. By nature, image signals are 2-dimensional and hence the significance of wavelet coefficients are 2-dimensionally distributed within each subband. After quantization, most coefficients become small integers or zeros. To a relatively large threshold, the whole scenery sees that significant coefficients sparsely and 2-dimensionally disperse in a wide tract of insignificant coefficients. Therefore, linear scan patterns inevitably cause losses in compression efficiency.","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132688435","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 Functional Compression through Graph Coloring","authors":"V. Doshi, D. Shah, M. Médard, S. Jaggi","doi":"10.1109/DCC.2007.34","DOIUrl":"https://doi.org/10.1109/DCC.2007.34","url":null,"abstract":"We consider the distributed computation of a function of random sources with minimal communication. Specifically, given two discrete memoryless sources, X and Y, a receiver wishes to compute f(X, Y) based on (encoded) information sent from X and Y in a distributed manner. A special case, f(X, Y) = (X, Y), is the classical question of distributed source coding considered by Slepian and Wolf (1973). Orlitsky and Roche (2001) considered a somewhat restricted setup when Y is available as side information at the receiver. They characterized the minimal rate at which X needs to transmit data to the receiver as the conditional graph entropy of the characteristic graph of X based on f. In our recent work (2006), we further established that this minimal rate can be achieved by means of graph coloring and distributed source coding (e.g. Slepian-Wolf coding). This characterization allows for the separation between \"function coding\" and \"correlation coding.\" In this paper, we consider a more general setup where X and Y are both encoded (separately). This is a significantly harder setup for which to give a single-letter characterization for the complete rate region. We find that under a certain condition on the support set of X and Y (called the zigzag condition), it is possible to characterize the rate region based on graph colorings at X and Y separately. That is, any achievable pair of rates can be realized by means of first coloring graphs at X and Y separately (function coding) and then using Slepian-Wolf coding for these colors (correlation coding). We also obtain a single-letter characterization of the minimal joint rate. Finally, we provide simulation results based on graph coloring to establish the rate gains on real sequences","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"82 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114332158","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":"Spatial Sparsity Induced Temporal Prediction for Hybrid Video Compression","authors":"Gang Hua, O. Guleryuz","doi":"10.1109/DCC.2007.71","DOIUrl":"https://doi.org/10.1109/DCC.2007.71","url":null,"abstract":"In this paper we propose a new motion compensated prediction technique that enables successful predictive encoding during fades, blended scenes, temporally decorrelated noise, and many other temporal evolutions which force predictors used in traditional hybrid video coders to fail. We model reference frame blocks to be used in motion compensated prediction as consisting of two superimposed parts: one part that is relevant for prediction and another part that is not relevant. By performing prediction in a domain where the video frames are spatially sparse, our work allows the automatic isolation of the prediction-relevant parts. These are then used to enable better prediction than would be possible otherwise. Our sparsity induced prediction algorithm (SIP) generates successful predictors by exploiting the non-convex structure of the sets that natural images and video frames lie in. Correctly determining this non-convexity through sparse representations allows better performance in hybrid video codecs equipped with the proposed work","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130607002","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":"Advanced Optimizations for VQ Compression on Parallel Systems","authors":"A. Wakatani","doi":"10.1109/DCC.2007.9","DOIUrl":"https://doi.org/10.1109/DCC.2007.9","url":null,"abstract":"Summary form only given. Recently, parallel processing technologies have been applied to a variety of applications to boost its performance. We implemented a parallel algorithm with \"double strip-mining\" method of optimal codeword search for VQ compression on a shared-memory parallel environment and evaluated the effectiveness of the parallel algorithm. Namely, by applying strip-mining method to a loop with regard to vectors as well as that to codewords (double strip-mining), the buffer area, which keeps the intermediate results, can be dramatically reduced and the results of our experiments find that this method can keep the same performance as the original strip-mining method in terms of the elapsed time.","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127771470","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 Compression of Colour Video Sequence using Optimal Prediction Theory - Octopus","authors":"S. Andriani, G. Calvagno","doi":"10.1109/DCC.2007.52","DOIUrl":"https://doi.org/10.1109/DCC.2007.52","url":null,"abstract":"Summary form only given. In this paper we present a lossless compression algorithm for colour video sequence which exploits the spatial, the spectral and the temporal correlations of a colour video sequence in the RGB colour space using the well-known optimal prediction theory. The main idea is to construct the optimal prediction coefficients estimating an autocorrelation matrix which exploits all these correlations. No colour transformation or motion compensation are applied because reversible colour transformations are not able to fully decorrelate the three bands of each frame, and motion compensation remarkably increases the complexity of the updating step of the autocorrelation matrix estimate. Furthermore, the fact that our algorithm is not based on motion compensation lead it to be robust to scene changes. The prediction errors are then coded using a context-based Golomb-Rice coder, with bias cancellation, but without run-length coding. To construct the contexts, the prediction errors are then modeled using an estimate of their local variance. This estimate considers all the previous prediction errors, using a forgetting factor to improve the adaptability of the proposed algorithm. The quantized estimated variance values are used as contexts for the Golomb-Rice coder, and, among others, we considered the following solutions: - 12 contexts estimated by sampling the standard deviation with a quantization step Delta = 1, and a saturation threshold equal to 12, [delta12]; - 128 contexts estimated by sampling the standard deviation with Delta = 1/3 and a saturation threshold equal to 128/3, [delta128]. The obtained coding results are presented for the proposed algorithm compared to JPEG-LS (without using any colour transformation), and JPEG2000 (in lossless mode, using the reversible colour YDbDr transform, and the 5/3 DWT). The results show an improvement of about 1.5 bpp and 0.65 bpp if compared with JPEG-LS coder, and JPEG2000, respectively","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132088768","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":"Dictionary-based English text compression using word endings","authors":"Jeehong Yang, S. Savari","doi":"10.1109/DCC.2007.31","DOIUrl":"https://doi.org/10.1109/DCC.2007.31","url":null,"abstract":"In this paper, we propose an dictionary-based English text compression algorithm; we name it star word ending (StarWE) because of certain similarities to the StarNT algorithm. Furthermore, StarWE borrows techniques that are used in WRT such as EOL coding, punctuation mark modeling, and n-gram matching. The main difference between StarWE and StarNT is in the division of the external dictionary; StarWE divides it by word endings so that the compressor would be able to obtain some of the tag information","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130824183","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":"The Wyner-Ziv Rate-Distortion Function of Multivariate Gaussian Sources and Its Application in Distributed Video Coding","authors":"Peng Wang, Jia Wang, Songyu Yu, Erkang Chen, Xiaokang Yang, Xiaodong Wang","doi":"10.1109/DCC.2007.77","DOIUrl":"https://doi.org/10.1109/DCC.2007.77","url":null,"abstract":"Wyner-Ziv coding is presented in this paper. It is extended to the scenario of multivariate source and side information, whose rate-distortion function is obtained by a reverse water-filling method for the joint quadratic-Gaussian case.","PeriodicalId":234035,"journal":{"name":"2007 Data Compression Conference (DCC'07)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124641444","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}