2018 Data Compression Conference最新文献

{"title":"Shearlet Transform Based Prediction Scheme for Light Field Compression","authors":"Waqas Ahmad, Suren Vagharshakyan, Mårten Sjöström, A. Gotchev, R. Bregović, R. Olsson","doi":"10.1109/DCC.2018.00049","DOIUrl":"https://doi.org/10.1109/DCC.2018.00049","url":null,"abstract":"Light field acquisition technologies capture angular and spatial information of the scene. The spatial and angular information enables various post processing applications, e.g. 3D scene reconstruction, refocusing, synthetic aperture etc at the expense of an increased data size. In this paper, we present a novel prediction tool for compression of light field data acquired with multiple camera system. The captured light field (LF) can be described using two plane parametrization as, L(u, v, s, t), where (u, v) represents each view image plane coordinates and (s, t) represents the coordinates of the capturing plane. In the proposed scheme, the captured LF is uniformly decimated by a factor d in both directions (in s and t coordinates), resulting in a sparse set of views also referred to as key views. The key views are converted into a pseudo video sequence and compressed using high efficiency video coding (HEVC). The shearlet transform based reconstruction approach, presented in [1], is used at the decoder side to predict the decimated views with the help of the key views. Four LF images (Truck, Bunny from Stanford dataset, Set2 and Set9 from High Density Camera Array dataset) are used in the experiments. Input LF views are converted into a pseudo video sequence and compressed with HEVC to serve as anchor. Rate distortion analysis shows the average PSNR gain of 0.98 dB over the anchor scheme. Moreover, in low bit-rates, the compression efficiency of the proposed scheme is higher compared to the anchor and on the other hand the performance of the anchor is better in high bit-rates. Different compression response of the proposed and anchor scheme is a consequence of their utilization of input information. In the high bit-rate scenario, high quality residual information enables the anchor to achieve efficient compression. On the contrary, the shearlet transform relies on key views to predict the decimated views without incorporating residual information. Hence, it has inherit reconstruction error. In the low bit-rate scenario, the bit budget of the proposed compression scheme allows the encoder to achieve high quality for the key views. The HEVC anchor scheme distributes the same bit budget among all the input LF views that results in degradation of the overall visual quality. The sensitivity of human vision system toward compression artifacts in low-bit-rate cases favours the proposed compression scheme over the anchor scheme.","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":"115778299","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":"Practical Succinct Text Indexes in External Memory","authors":"Hongwei Huo, Xiaoyang Chen, Yuhao Zhao, Xiaojin Zhu, J. Vitter","doi":"10.1109/DCC.2018.00030","DOIUrl":"https://doi.org/10.1109/DCC.2018.00030","url":null,"abstract":"Chien et al. [1, 2] introduced the geometric Burrows-Wheeler transform (GBWT) as the first succinct text index for I/O-efficient pattern matching in external memory; it operates by transforming a text T into point set S in the two-dimensional plane. In this paper we introduce a practical succinct external memory text index, called mKD-GBWT. We partition S into ς2 subregions by partitioning the x-axis into ς intervals using the suffix ranges of characters of T and partitioning the y-axis into ς intervals using characters of T, where ς is the alphabet size of T. In this way, we can represent a point using fewer bits and perform a query in a reduced region so as to improve the space usage and I/Os of GBWT in practice. In addition, we plug a crit-bit tree into each node of string B-trees to represent variable-length strings stored. Experimental results show that mKD-GBWT provides significant improvement in space usage compared with the state-of-the-art indexing techniques. The source code is available online [3].","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"36 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":"131192097","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":"Rate-Distortion-Complexity Optimized Coding Scheme for Kvazaar HEVC Intra Encoder","authors":"Ari Lemmetti, E. Kallio, Marko Viitanen, Jarno Vanne, T. Hämäläinen","doi":"10.1109/DCC.2018.00072","DOIUrl":"https://doi.org/10.1109/DCC.2018.00072","url":null,"abstract":"This paper summarizes a low-complexity rate-distortion optimization (RDO) scheme for Kvazaar HEVC intra encoder (github.com/ultravideo/kvazaar). Our work particularly addresses RDO quantization (RDOQ) since it is the most complex intra coding tool taking almost 60% of the Kvazaar complexity.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"111 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":"116783206","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":"SPDP: An Automatically Synthesized Lossless Compression Algorithm for Floating-Point Data","authors":"S. Claggett, S. Azimi, Martin Burtscher","doi":"10.1109/DCC.2018.00042","DOIUrl":"https://doi.org/10.1109/DCC.2018.00042","url":null,"abstract":"Scientific computing produces, transfers, and stores massive amounts of single- and double-precision floating-point data, making this a domain that can greatly benefit from data compression. To gain insight into what makes an effective lossless compression algorithm for such data, we generated over nine million algorithms and selected the one that yields the highest compression ratio on 26 datasets. The resulting algorithm, called SPDP, comprises four data transformations that operate exclusively at word or byte granularity. Nevertheless, SPDP delivers the highest compression ratio on eleven datasets and, on average, outperforms all but one of the seven compared compressors. An analysis of SPDP's internals reveals how to build effective compression algorithms for scientific data.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"24 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":"131517692","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":"A New HEVC In-Loop Filter Based on Multi-channel Long-Short-Term Dependency Residual Networks","authors":"Xiandong Meng, Chen Chen, Shuyuan Zhu, B. Zeng","doi":"10.1109/DCC.2018.00027","DOIUrl":"https://doi.org/10.1109/DCC.2018.00027","url":null,"abstract":"In this paper, we propose a new HEVC in-loop filter based on a multi-channel long-short-term dependency residual network (MLSDRN). Inspired by the information storage and information update function of human memory cell, our MLSDRN introduces an update cell to adaptively store and select the long-term and short-term dependency information through an adaptive learning process. In addition, we leverage the block boundary information that recorded in the bit-streams to improve the filter performance, which also makes our MLSDRN to unequally treat the video content. Meanwhile, the multi-channel is introduced to solve the illumination discrepancy problem. We integrate the novel in-loop filter into HM reference software, and applying it to luma and chroma components, simulation results demonstrate that the proposed in-loop filter can save BD-rate reduction up to 15.9% with ALF off. For luma component, the novel in-loop filter achieves 6.0%, 8.1%, 7.4% BD-rate saving for all intra, low delay and random access configurations, respectively.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"19 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":"134241454","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":"Entropy Coding and Entropy Coding Improvements of JPEG XS","authors":"T. Richter, J. Keinert, A. Descampe, G. Rouvroy","doi":"10.1109/DCC.2018.00017","DOIUrl":"https://doi.org/10.1109/DCC.2018.00017","url":null,"abstract":"JPEG XS is a new standard for low-latency and low-complexity coding designed by the JPEG committee. Unlike former developments, optimal rate distortion performance is only a secondary goal; the focus of JPEG~XS is to enable cost-efficient, easy to parallelize implementations suitable for FPGAs or GPUs. In this article, we shed some light on the entropy coding back-end of JPEG~XS and introduce modifications of the entropy coding stage currently under discussion that improve objective and subjective quality of the compressed images without compromising the parallelism of the original algorithm.","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":"127620159","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":"Exploiting Computation-Friendly Graph Compression Methods for Adjacency-Matrix Multiplication","authors":"Alexandre P. Francisco, T. Gagie, Susana Ladra, G. Navarro","doi":"10.1109/DCC.2018.00039","DOIUrl":"https://doi.org/10.1109/DCC.2018.00039","url":null,"abstract":"Computing the product of the (binary) adjacency matrix of a large graph with a real-valued vector is an important operation that lies at the heart of various graph analysis tasks, such as computing PageRank. In this paper we show that some well-known Web and social graph compression formats are computation-friendly, in the sense that they allow boosting the computation. In particular, we show that the format of Boldi and Vigna allows computing the product in time proportional to the compressed graph size. Our experimental results show speedups of at least 2 on graphs that were compressed at least 5 times with respect to the original. We show that other successful graph compression formats enjoy this property as well.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"65 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120860083","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":"Film Grain Synthesis for AV1 Video Codec","authors":"A. Norkin, N. Birkbeck","doi":"10.1109/DCC.2018.00008","DOIUrl":"https://doi.org/10.1109/DCC.2018.00008","url":null,"abstract":"Film grain is abundant in TV and movie content. It is often part of the creative intent and needs to be preserved while encoding. However, the random nature of film grain is difficult to compress using traditional coding tools. This paper describes a film grain modeling and synthesis algorithm proposed for the AV1 video codec. At the encoder, an autoregressive model of film grain is transmitted relative to a denoised signal, and the film grain strength is modeled as a function of intensity. The corresponding renoising at the decoder is implemented using an efficient block-based approach suitable for use in consumer electronic devices. Preliminary results indicate that the approach can give significant bitrate savings (up to 50%) on sequences with heavy film grain.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"13 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":"121225402","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":"A Hybrid Approach for Wind Tunnel Data Compression","authors":"Jin Zhou, C. Kwan","doi":"10.1109/DCC.2018.00088","DOIUrl":"https://doi.org/10.1109/DCC.2018.00088","url":null,"abstract":"A novel and hybrid data compression framework is proposed to compress wind tunnel data. Both lossless and lossy compression can be performed.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"226 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":"131676021","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":"Hybrid Sensor Network Data Compression with Error Resiliency","authors":"C. Kwan, Yvonne Luk","doi":"10.1109/DCC.2018.00069","DOIUrl":"https://doi.org/10.1109/DCC.2018.00069","url":null,"abstract":"We propose a high performance compression system with error resilience for sensor networks.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"24 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":"116026764","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}