{"title":"DCT Domain Message Embedding in Spread-Spectrum Steganography System","authors":"Neha Agrawal, Anubha Gupta","doi":"10.1109/DCC.2009.86","DOIUrl":"https://doi.org/10.1109/DCC.2009.86","url":null,"abstract":"Spread-spectrum steganographic (SSIS) method offers high payload and robustness to additive noise in transmission channel but the visual quality of image is distorted and exact data recovery may not be satisfied. DCT-domain message hiding based steganographic techniques provide high image imperceptibility and exact data recovery in absence of noise. In this paper, we combined the best of SSIS and DCT-domain hiding to provide high image imperceptibility and robustness to noise. We demonstrate our proposed algorithm through experiments on additive noise and jpeg compression attacks in the transmitted channel.","PeriodicalId":377880,"journal":{"name":"2009 Data Compression Conference","volume":"PP 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126401977","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":"Binary Alpha-Plane Assisted Fast Motion Estimation of Video Objects in Wavelet Domain","authors":"Chuanming Song, Xiang-Hai Wang, Yanwen Guo, Fuyan Zhang","doi":"10.1109/DCC.2009.32","DOIUrl":"https://doi.org/10.1109/DCC.2009.32","url":null,"abstract":"In this paper, we present a novel approach to motion estimation (ME) of arbitrarily shaped video objects in wavelet domain. We explore the guiding role of binary alpha-plane in assisting ME of video objects and first devise a new block matching scheme of alpha-plane, by exploiting boundary expansion and boundary masks. To eliminate shift-variance, we modify low-band-shift (LBS) method via substituting variable-size block for wavelet block. Combining the modified LBS with a hierarchical structure, we further present a multiscale ME approach. Extensive experiments show that the proposed approach outperforms most of previous methods in terms of both subjective quality and objective quality. Moreover, significant reduction is achieved in computational complexity (89.05% at most) and memory requirement.","PeriodicalId":377880,"journal":{"name":"2009 Data Compression Conference","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131554744","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":"Complex Wavelet Modulation Subbands for Speech Compression","authors":"J. Luneau, J. Lebrun, S. H. Jensen","doi":"10.1109/DCC.2009.52","DOIUrl":"https://doi.org/10.1109/DCC.2009.52","url":null,"abstract":"Low-frequency modulation of sound carry essential information for speech and music. They must be preserved for compression. The complex modulation spectrum is commonly obtained by spectral analysis of the sole temporal envelopes of the subbands out of a time/frequency analysis. Amplitudes and tones of speech or music tend to vary slowly over time thus the temporal envelopes are mostly of polynomial type. Processing in this domain usually creates undesirable distortions because only the magnitudes are taken into account and the phase data is often neglected. We remedy this problem with the use of a complex wavelet transform as a more appropriate envelope and phase processing tool. Complex wavelets carry both magnitude and phase explicitly with great sparsity and preserve well polynomials. Moreover an analytic Hilbert-like transform is possible with complex wavelets implemented as an orthogonal filter bank. By working in this alternative transform domain coined as ``Modulation Subbands\", this transform shows very promising compression capabilities thanks to interesting sparsity properties and suggests new approaches for joint spectro-temporal analytic processing of slow frequency and phase varying audio signals.","PeriodicalId":377880,"journal":{"name":"2009 Data Compression Conference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131367806","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":"Probing the Randomness of Proteins by Their Subsequence Composition","authors":"A. Apostolico, F. Cunial","doi":"10.1109/DCC.2009.60","DOIUrl":"https://doi.org/10.1109/DCC.2009.60","url":null,"abstract":"The quantitative underpinning of the information contents of biosequences represents an elusive goal and yet also an obvious prerequisite to the quantitative modeling and study of biological function and evolution. Previous studies have consistently exposed a tenacious lack of compressibility on behalf of biosequences. This leaves the question open as to what distinguishes them from random strings, the latter being clearly unpalatable to the living cell. This paper assesses the randomness of biosequences in terms on newly introduced parameters that relate to the vocabulary of their (suitably constrained) subsequences rather than their substrings. Results from experiments show the potential of the method in distinguishing a protein sequence from its random reshuffling, as well as in tasks of classification and clustering.","PeriodicalId":377880,"journal":{"name":"2009 Data Compression Conference","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125666978","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":"Suffix Tree Based VF-Coding for Compressed Pattern Matching","authors":"T. Kida","doi":"10.1109/DCC.2009.58","DOIUrl":"https://doi.org/10.1109/DCC.2009.58","url":null,"abstract":"We propose an efficient variable-length-to-fixed-length code (VF code for short), called ST-VF code. It utilizes a frequency-base-pruned suffix tree as a parse tree. VF codes as typified by Tunstall code have a preferable aspect to compressed pattern matching. It is unnecessary to distinguish code boundaries on compressed texts since all codewords have the same length.","PeriodicalId":377880,"journal":{"name":"2009 Data Compression Conference","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132421859","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":"Compressed Kernel Perceptrons","authors":"S. Vucetic, Vladimir Coric, Zhuang Wang","doi":"10.1109/DCC.2009.75","DOIUrl":"https://doi.org/10.1109/DCC.2009.75","url":null,"abstract":"Kernel machines are a popular class of machine learning algorithms that achieve state of the art accuracies on many real-life classification problems. Kernel perceptrons are among the most popular online kernel machines that are known to achieve high-quality classification despite their simplicity. They are represented by a set of B prototype examples, called support vectors, and their associated weights. To obtain a classification, a new example is compared to the support vectors. Both space to store a prediction model and time to provide a single classification scale as O(B). A problem with kernel perceptrons is that on noisy data the number of support vectors tends to grow without bounds with the number of training examples. To reduce the strain at computational resources, budget kernel perceptrons have been developed by upper bounding the number of support vectors. In this work, we propose a new budget algorithm that upper bounds the number of bits needed to store kernel perceptron. Setting the bitlength constraint could facilitate development of hardware and software implementations of kernel perceptrons on resource-limited devices such as microcontrollers. The proposed compressed kernel perceptron algorithm decides on the optimal tradeoff between number of support vectors and their bit precision. The algorithm was evaluated on several benchmark data sets and the results indicate that it can train highly accurate classifiers even when the available memory budget is below 1 Kbit. This promising result points to a possibility of implementing powerful learning algorithms even on the most resource-constrained computational devices.","PeriodicalId":377880,"journal":{"name":"2009 Data Compression Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130408876","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}
Wei-ling Chang, Xiao-chun Yun, Binxing Fang, Shupeng Wang
{"title":"The Block LZSS Compression Algorithm","authors":"Wei-ling Chang, Xiao-chun Yun, Binxing Fang, Shupeng Wang","doi":"10.1109/DCC.2009.9","DOIUrl":"https://doi.org/10.1109/DCC.2009.9","url":null,"abstract":"The mainstream compression algorithms, such as LZ, Huffman, PPM etc., have been extensively studied in recent years. However, rather less attention has been paid to the block algorithm of those algorithms. The aim of this study was therefore to investigate the block LZSS. We studied the relationship between the compression ratio of block LZSS and the value of index or length. We found that the bit of length has little effect on the compression performance of block LZSS, and the bit of index has a significant effect on the compression ratio. Results of the experiment show that to obtain better efficiency from block LZSS, a moderate sized block which is greater than 32KiB, may be optimal, and the optimal block size does not depend on file types. We also investigated factors which affect the optimal block size. We use the mean block standard deviation (MBS) and locality of reference to measure the compression ratio. we found that good data locality implies a large skew in the data distribution, and the greater data distribution skew or the MBS, the better the compression ratio.","PeriodicalId":377880,"journal":{"name":"2009 Data Compression Conference","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130753974","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":"How Can Intra Correlation Be Exploited Better?","authors":"Feng Wu, Xiulian Peng, Jizheng Xu, Shipeng Li","doi":"10.1109/DCC.2009.23","DOIUrl":"https://doi.org/10.1109/DCC.2009.23","url":null,"abstract":"This paper studies how to better exploit intra correlation in image/intra-frame coding. Unlike the previous assumption, where the correlation between samples is determined only by their distance, image is considered to present local orientation property. The correlation between samples is determined by not only the distance but also the link orientation. Thus, a directional Filtering Transform (dFT) is proposed in this paper to exploit local orientation correlation among samples. It consists of directional filtering and optional transform. Furthermore, this paper analyzes the theoretical coding gain of the proposed directional filtering in terms of signal power spectral density (PSD). Both numerical results and actual compression results of the intra-frame coding in H.264 demonstrate the advantages of the proposed dFT with decoding complexity increased slightly.","PeriodicalId":377880,"journal":{"name":"2009 Data Compression Conference","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132949424","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":"Overlapped Tiling for Fast Random Oblique Plane Access of 3D Object Datasets","authors":"Zihong Fan, Antonio Ortega, Cheng-hao Chien","doi":"10.1109/DCC.2009.83","DOIUrl":"https://doi.org/10.1109/DCC.2009.83","url":null,"abstract":"Volume visualization with random data access poses significant challenges. While tiling techniques lead to simple implementations, they are not well suited for cases where the goal is to access arbitrarily located subdimensional datasets (e.g., being able to display an arbitrary 2D planar “cut” from a 3D volume). Significant effort has been devoted to volumetric data compression, with most techniques proposing to tile volumes into cuboid subvolumes to enable random access. In this paper we show that, in cases where subdimensional datasets are accessed, this leads to significant transmission inefficiency. As an alternative, we propose novel serverclient based data representation and retrieval methods which can be used for fast random access of oblique plane from 3D volume datasets. In this paper, 3D experiments are shown but the approach may be extended to higher dimensional datasets. We use multiple redundant tilings of the 3D object, where each tiling has a different orientation.We discuss the 3D rectangular tiling scheme and two main algorithm components of such 3D system, namely, (i) a search algorithm to determine which tiles should be retrieved for a given query and (ii) a mapping algorithm to enable efficient encoding without interpolation of rotated tiles. In exchange for increased server storage, we demonstrate that significant reductions in average transmission rate can be achieved relative to conventional cubic tiling techniques, e.g., nearly 40% reduction in average transmission rate for less than a factor of twenty overhead in storage before compression. Note that, as shown in our earlier work on the 2D case, the storage overhead will be lower after compression (e.g., in 2D the relative increase in storage in the compressed domain was at least a factor of two lower than in the uncompressed domain.)","PeriodicalId":377880,"journal":{"name":"2009 Data Compression Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122903842","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":"Algorithmic Cross-Complexity and Relative Complexity","authors":"D. Cerra, M. Datcu","doi":"10.1109/DCC.2009.6","DOIUrl":"https://doi.org/10.1109/DCC.2009.6","url":null,"abstract":"Information content and compression are tightly related concepts that can be addressed by classical and algorithmic information theory. Several entities in the latter have been defined relying upon notions of the former, such as entropy and mutual information, since the basic concepts of these two approaches present many common tracts. In this work we further expand this parallelism by defining the algorithmic versions of cross-entropy and relative entropy (or Kullback-Leiblerdivergence), two well-known concepts in classical information theory. We define the cross-complexity of an object x with respect to another object y as the amount of computational resources needed to specify x in terms of y, and the complexity of x related to y as the compression power which is lost when using such a description for x, with respect to its shortest representation. Since the main drawback of these concepts is their uncomputability, a suitable approximation based on data compression is derived for both and applied to real data. This allows us to improve the results obtained by similar previous methods which were intuitively defined.","PeriodicalId":377880,"journal":{"name":"2009 Data Compression Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121155848","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}
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
