发布求助
文献互助 智能选刊 最新文献

2018 Data Compression Conference最新文献

筛选
英文 中文
Joint Source-Channel Coding with Neural Networks for Analog Data Compression and Storage 联合源信道编码与神经网络模拟数据压缩与存储
2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00023
Ryan Zarcone, Dylan M. Paiton, Alexander G. Anderson, Jesse Engel, H. P. Wong, B. Olshausen
{"title":"Joint Source-Channel Coding with Neural Networks for Analog Data Compression and Storage","authors":"Ryan Zarcone, Dylan M. Paiton, Alexander G. Anderson, Jesse Engel, H. P. Wong, B. Olshausen","doi":"10.1109/DCC.2018.00023","DOIUrl":"https://doi.org/10.1109/DCC.2018.00023","url":null,"abstract":"We provide an encoding and decoding strategy for efficient storage of analog data onto an array of Phase-Change Memory (PCM) devices. The PCM array is treated as an analog channel, with the stochastic relationship between write voltage and read resistance for each device determining its theoretical capacity. The encoder and decoder are implemented as neural networks with parameters that are trained end-to-end to minimize distortion for a fixed number of devices. To minimize distortion, the encoder and decoder must adapt jointly to the statistics of images and the statistics of the channel. Similar to Balle et al. (2017), we find that incorporating divisive normalization in the encoder, paired with de-normalization in the decoder, improves model performance. We show that the autoencoder achieves a rate-distortion performance above that achieved by a separate JPEG source coding and binary channel coding scheme. These results demonstrate the feasibility of exploiting the full analog dynamic range of PCM or other emerging memory devices for efficient storage of analog image data.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117302960","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}
引用次数: 17
Enhanced Intra Prediction with Recurrent Neural Network in Video Coding 视频编码中基于递归神经网络的增强帧内预测
2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00066
Yueyu Hu, Wenhan Yang, Sifeng Xia, Wen-Huang Cheng, Jiaying Liu
{"title":"Enhanced Intra Prediction with Recurrent Neural Network in Video Coding","authors":"Yueyu Hu, Wenhan Yang, Sifeng Xia, Wen-Huang Cheng, Jiaying Liu","doi":"10.1109/DCC.2018.00066","DOIUrl":"https://doi.org/10.1109/DCC.2018.00066","url":null,"abstract":"Intra prediction is one of the important parts in video/image codec. With intra prediction mechanism, spatial redundancy can be largely removed for further bit saving. However, current state-of-the-art intra prediction method does not produce satisfactory prediction result due to its limits in reference samples and modeling ability. To enhance the intra prediction in HEVC, in this paper, a deep neural network featuring spatial RNN, which models the spatial dependency of pixels as sequential dynamics, is proposed to generate better prediction signals. Experimental results show improvement in BD-Rate for the proposed method compared with the original HEVC prediction scheme.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117036481","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}
引用次数: 24
Compressed Hierarchical Clustering 压缩分层聚类
2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00052
Gilad Baruch, Dana Shapira, S. T. Klein
{"title":"Compressed Hierarchical Clustering","authors":"Gilad Baruch, Dana Shapira, S. T. Klein","doi":"10.1109/DCC.2018.00052","DOIUrl":"https://doi.org/10.1109/DCC.2018.00052","url":null,"abstract":"Hierarchical Clustering is widely used in Machine Learning and Data Mining. It stores bit-vectors in the nodes of a k-ary tree, usually without trying to compress them. We suggest a double usage of the {sf xor}ing operations defining the Hamming distance used in the clustering process, extending it also to be used to transform the vector in one node into a more compressible form, as a function of the vector in the parent node. Compression is then achieved by run-length encoding, followed by optional Huffman coding, and we show how the compressed file may be processed directly, without decompression.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125334931","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}
引用次数: 0
Fast and Efficient Compression of Next Generation Sequencing Data 下一代测序数据的快速高效压缩
2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00055
C. Constantinescu, G. Schmidt
{"title":"Fast and Efficient Compression of Next Generation Sequencing Data","authors":"C. Constantinescu, G. Schmidt","doi":"10.1109/DCC.2018.00055","DOIUrl":"https://doi.org/10.1109/DCC.2018.00055","url":null,"abstract":"Despite the large number of research papers and compression algorithms proposed for com- pressing FASTQ genomic data generated by sequencing machines, by far the most commonly used compression algorithm in the industry for FASTQ data is gzip. The main drawback of the proposed alternative special-purpose compression algorithms is the slow speed of either compression or de- compression or both, and also their brittleness by making various limiting assumptions about the input FASTQ format (for example, the structure of the headers or fixed lengths of the records [5]) in order to further improve their specialized compression. In this paper we propose using a simple modeling improvement combined with fast general purpose encoders, achieving 4X-40X speed improvements and 6%-11% more efficienct compression ratios for the compression and decompression of FASTQ genomic data compared to gzip and also being 5X faster than special-purpose FASTQ compressors like DSRC v2 and slimfastq.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126401938","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}
引用次数: 2
Task-Based JPEG 2000 Image Compression: An Information-Theoretic Approach 基于任务的JPEG 2000图像压缩:一种信息论方法
2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00076
Yuzhang Lin, A. Ashok, M. Marcellin, A. Bilgin
{"title":"Task-Based JPEG 2000 Image Compression: An Information-Theoretic Approach","authors":"Yuzhang Lin, A. Ashok, M. Marcellin, A. Bilgin","doi":"10.1109/DCC.2018.00076","DOIUrl":"https://doi.org/10.1109/DCC.2018.00076","url":null,"abstract":"Traditional image compression methods primarily focus on maximizing the fidelity of the compressed image using image quality driven distortion metrics, which are ideally suited for human observers but are not necessarily optimal for machine observers, i.e., automated image exploitation algorithms. For machine observers, task-based distortion metrics, such as probability of error, have been shown to be more effective for tasks such as object detection and classification. This motivates an approach to a task-based image compression, within the JPEG 2000 framework, which preserves the information that is most relevant for the given task. Our proposed method produces a JPEG 2000 compliant compressed codestream, which can be decoded by any JPEG 2000 compliant decoder. We demonstrate the feasibility and the effectiveness of our task-based image compression approach on a simple object classification and detection problem and quantify its performance relative to a conventional MSE encoder.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121999227","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}
引用次数: 11
Lossy Compression of Quality Scores in Differential Gene Expression: A First Assessment and Impact Analysis 差异基因表达质量分数的有损压缩:首次评估和影响分析
2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00025
Ana A. Hernandez-Lopez, Jan Voges, C. Alberti, M. Mattavelli, J. Ostermann
{"title":"Lossy Compression of Quality Scores in Differential Gene Expression: A First Assessment and Impact Analysis","authors":"Ana A. Hernandez-Lopez, Jan Voges, C. Alberti, M. Mattavelli, J. Ostermann","doi":"10.1109/DCC.2018.00025","DOIUrl":"https://doi.org/10.1109/DCC.2018.00025","url":null,"abstract":"High-throughput sequencing of RNA molecules has enabled the quantitative analysis of gene expression at the expense of storage space and processing power. To alleviate these problems, lossy compression methods of the quality scores associated to RNA sequencing data have recently been proposed, and the evaluation of their impact on downstream analyses is gaining attention. In this context, this work presents a first assessment of the impact of lossily compressed quality scores in RNA sequencing data on the performance of some of the most recent tools used for differential gene expression.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122076720","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}
引用次数: 2
Efficient AV1 Video Coding Using a Multi-layer Framework 基于多层框架的高效AV1视频编码
2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00045
Wei-Ting Lin, Zoe Liu, D. Mukherjee, Jingning Han, Paul Wilkins, Yaowu Xu, K. Rose
{"title":"Efficient AV1 Video Coding Using a Multi-layer Framework","authors":"Wei-Ting Lin, Zoe Liu, D. Mukherjee, Jingning Han, Paul Wilkins, Yaowu Xu, K. Rose","doi":"10.1109/DCC.2018.00045","DOIUrl":"https://doi.org/10.1109/DCC.2018.00045","url":null,"abstract":"This paper proposes a multi-layer multi-reference prediction framework for effective video compression. Current AOM/AV1 baseline uses three reference frames for the inter prediction of each video frame. This paper first presents a new coding tool that extends the total number of reference frames in both forward and backward prediction directions. A multi-layer framework is then described, which suggests the encoder design and places different reference frames within one Golden Frame (GF) group to different layers. The multi-layer framework leverages the existing coding tools in the AV1 baseline, including the tool of \"show_existing_frame\" and the reference frame buffer update module of a wide flexibility. The use of extended ALTREF_FRAMEs is proposed, and multiple ALTREF_FRAME candidates are selected and widely spaced within one GF group. ALTREF_FRAME is a constructed, no-show reference obtained through temporal filtering of a look-ahead frame. In the multi-layer structure, one reference frame may serve different roles for the encoding of different frames through the virtual index manipulation. The experimental results have been collected over several video test sets of various resolutions and characteristics both texture- and motion-wise, which demonstrate that the proposed approach achieves a consistent coding gain compared to the AV1 baseline. For instance, using PSNR as the distortion metric, an average bitrate saving of 5.57+% in BDRate is obtained for the CIF-level resolution set, some of which has a gain of up to 13+%, and 4.47% on average for the VGA-level resolution set, some of which up to 18+%.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122357278","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}
引用次数: 8
The Bits Between Proteins 蛋白质之间的比特
2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00026
D. Sumanaweera, L. Allison, A. S. Konagurthu
{"title":"The Bits Between Proteins","authors":"D. Sumanaweera, L. Allison, A. S. Konagurthu","doi":"10.1109/DCC.2018.00026","DOIUrl":"https://doi.org/10.1109/DCC.2018.00026","url":null,"abstract":"Comparison of protein sequences via alignment is an important routine in modern biological studies. Although the technologies for aligning proteins are mature, the current state of the art continues to be plagued by many shortcomings, chiefly due to the reliance on: (i) naive objective functions, (ii) fixed substitution scores independent of the sequences being considered, (iii) arbitrary choices for gap costs, and (iv) reporting, often, one optimal alignment without a way to recognise other competing sequence alignments. Here, we address these shortcomings by applying the compression-based Minimum Message Length (MML) inference framework to the protein sequence alignment problem. This grounds the problem in statistical learning theory, handles directly the complexity-vs-fit trade-off without ad hoc gap costs, allows unsupervised inference of all the statistical parameters, and permits the visualization and exploration of competing sequence alignment landscape.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"3 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120843668","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}
引用次数: 3
Co-located Reference Frame Interpolation Using Optical Flow Estimation for Video Compression 基于光流估计的视频压缩共定位参考帧插值
2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00009
Bohan Li, Jingning Han, Yaowu Xu
{"title":"Co-located Reference Frame Interpolation Using Optical Flow Estimation for Video Compression","authors":"Bohan Li, Jingning Han, Yaowu Xu","doi":"10.1109/DCC.2018.00009","DOIUrl":"https://doi.org/10.1109/DCC.2018.00009","url":null,"abstract":"The hierarchical coding structure that supports bi-directional motion compensated prediction is commonly used for video compression efficiency. Conventional approach directly seeks the reference pixel block from each individual reference frame and use it or its linear combinations for prediction. It largely ignores the motion information between these reference frames. To fully utilize all the information from the bi-directional reference frames, this work builds a per-pixel motion field that connects the two-sided reference frames using optical flow estimation. A reference frame is then interpolated at the current frame location. This collocated reference frame effectively accounts for the true motion trajectories in the video signal including both translational and the more complex non-translational motion models, which are beyond the capability of the conventional block-based motion compensated prediction. The scheme is experimentally shown to provide substantial compression performance gains. A number of optimization designs are proposed to make the codec complexity feasible while largely maintaining the coding performance.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123832549","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}
引用次数: 11
A Dynamic Compressed Self-Index for Highly Repetitive Text Collections 高度重复文本集合的动态压缩自索引
2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00037
T. Nishimoto, Yoshimasa Takabatake, Yasuo Tabei
{"title":"A Dynamic Compressed Self-Index for Highly Repetitive Text Collections","authors":"T. Nishimoto, Yoshimasa Takabatake, Yasuo Tabei","doi":"10.1109/DCC.2018.00037","DOIUrl":"https://doi.org/10.1109/DCC.2018.00037","url":null,"abstract":"We present a novel compressed dynamic self-index for highly repetitive text collections. Signature encoding, an existing self-index of this type, has a large disadvantage of slow pattern search for short patterns. We obtain faster pattern search by leveraging the idea behind a truncated suffix tree (TST) to develop the first compressed dynamic self-index, called the TST-index, that supports not only fast pattern search but also dynamic update operations for highly repetitive texts. Experiments with a benchmark dataset show that the pattern search performance of the TST-index is significantly improved.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130001813","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}
引用次数: 3
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信