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On Lempel-Ziv Decompression in Small Space 小空间的Lempel-Ziv解压缩
2019 Data Compression Conference (DCC) Pub Date : 2019-03-01 DOI: 10.1109/DCC.2019.00030
S. Puglisi, Massimiliano Rossi
{"title":"On Lempel-Ziv Decompression in Small Space","authors":"S. Puglisi, Massimiliano Rossi","doi":"10.1109/DCC.2019.00030","DOIUrl":"https://doi.org/10.1109/DCC.2019.00030","url":null,"abstract":"Lempel-Ziv (LZ77) parsing is a powerful tool for data compression that has been the subject of intense research in the past 40 years and is now used in popular and widely-used compression software and as part of larger software systems. In this paper we study algorithms to efficiently decompress strings from the LZ parsing that use working memory proportional to the size, z, of the parsing itself, not that of the output string, n, as is the usual case. The only work we are aware of on this problem is recent and due to Bille~et~al. who describe an algorithm using O(n log^δσ time and O(z log^1 - δσ) space for any 0 ≤ δ ≤ 1. We provide the first implementation and experimental analysis of Bille~et~al.'s approach. Our results show that this approach, when implemented as described, is extremely slow in practice compared to the naive decompression algorithm, and uses lots of space. To remedy this we introduce several novel optimizations that drastically improve performance and lead to relevant space-time tradeoffs in practice on all datasets we tested.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"2018 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124991376","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
CNN-Based Driving of Block Partitioning for Intra Slices Encoding 基于cnn的片内编码块划分驱动
2019 Data Compression Conference (DCC) Pub Date : 2019-03-01 DOI: 10.1109/DCC.2019.00024
Franck Galpin, Fabien Racapé, S. Jaiswal, P. Bordes, F. L. Léannec, E. François
{"title":"CNN-Based Driving of Block Partitioning for Intra Slices Encoding","authors":"Franck Galpin, Fabien Racapé, S. Jaiswal, P. Bordes, F. L. Léannec, E. François","doi":"10.1109/DCC.2019.00024","DOIUrl":"https://doi.org/10.1109/DCC.2019.00024","url":null,"abstract":"This paper provides a technical overview of a deep-learning-based encoder method aiming at optimizing next generation hybrid video encoders for driving the block partitioning in intra slices. An encoding approach based on Convolutional Neural Networks is explored to partly substitute classical heuristics-based encoder speed-ups by a systematic and automatic process. The solution allows controlling the trade-off between complexity and coding gains, in intra slices, with one single parameter. This algorithm was proposed at the Call for Proposals of the Joint Video Exploration Team (JVET) on video compression with capability beyond HEVC. In All Intra configuration, for a given allowed topology of splits, a speed-up of ×2 is obtained without BD-rate loss, or a speed-up above ×4 with a loss below 1% in BD-rate.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126937025","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: 27
Perceptual Video Coding Based on Visual Saliency Modulated Just Noticeable Distortion 基于视觉显著性调制刚好可察觉失真的感知视频编码
2019 Data Compression Conference (DCC) Pub Date : 2019-03-01 DOI: 10.1109/DCC.2019.00077
Jing Cui, Ruiqin Xiong, Xinfeng Zhang, Shanshe Wang, Siwei Ma
{"title":"Perceptual Video Coding Based on Visual Saliency Modulated Just Noticeable Distortion","authors":"Jing Cui, Ruiqin Xiong, Xinfeng Zhang, Shanshe Wang, Siwei Ma","doi":"10.1109/DCC.2019.00077","DOIUrl":"https://doi.org/10.1109/DCC.2019.00077","url":null,"abstract":"To reduce the perceptual redundancy in the video coding process, human visual system (HVS)-based visual attention and visual sensitivity can be utilized due to their intrinsic natures. Just Noticeable Distortion (JND) is one of widely used models to simulate human visual sensitivity, while visual saliency map has been popular for years in image processing to describe the visual attention feature, which has been proved by the ability to enhance the visual sensitivity effect. In this paper, we proposed a perceptual video coding (PVC) scheme with visual saliency modulated JND model to suppress the DCT coefficient without resulting in noteworthy subjective quality degradation. The experimental results show that the PVC scheme with the proposed VS-JND model can save bit rates up to 35.58% in high bit rates case with the similar subjective quality compared with that of HEVC software reference code HM 16.12.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"5 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133356021","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: 9
Wave Atoms for Lossy Compression of Digital Holograms 用于数字全息图有损压缩的波原子
2019 Data Compression Conference (DCC) Pub Date : 2019-03-01 DOI: 10.1109/DCC.2019.00048
Tobias Birnbaum, Ayyoub Ahar, David Blinder, C. Schretter, T. Kozacki, P. Schelkens
{"title":"Wave Atoms for Lossy Compression of Digital Holograms","authors":"Tobias Birnbaum, Ayyoub Ahar, David Blinder, C. Schretter, T. Kozacki, P. Schelkens","doi":"10.1109/DCC.2019.00048","DOIUrl":"https://doi.org/10.1109/DCC.2019.00048","url":null,"abstract":"Compression of digital holograms is a major challenge that needs to be resolved to enable the efficient storage, transmission and rendering of macroscopic holographic signals. In this work, we propose to deploy the wave atom transform that has been utilized before for interferometric modalities such as acoustic and seismic signals. This non-adaptive multiresolution transform has good space-frequency localization and its orthonormal basis is suitable for sparsifying holographic signals. By replacing the CDF 9/7 wavelet transform stage in a JPEG 2000 codec with the proposed wave atom transform, we did assess its suitability for coding complex amplitude wavefronts. Experimental results demonstrate improved rate-distortion performance with respect to JPEG 2000 and H.265/HEVC for a set of computer-generated, diffuse, macroscopic holograms.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128569878","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: 6
History-Based Motion Vector Prediction in Versatile Video Coding 多用途视频编码中基于历史的运动矢量预测
2019 Data Compression Conference (DCC) Pub Date : 2019-03-01 DOI: 10.1109/DCC.2019.00012
Li Zhang, Kai Zhang, Hongbin Liu, Hsiao-Chiang Chuang, Yue Wang, Jizheng Xu, Pengwei Zhao, Dingkun Hong
{"title":"History-Based Motion Vector Prediction in Versatile Video Coding","authors":"Li Zhang, Kai Zhang, Hongbin Liu, Hsiao-Chiang Chuang, Yue Wang, Jizheng Xu, Pengwei Zhao, Dingkun Hong","doi":"10.1109/DCC.2019.00012","DOIUrl":"https://doi.org/10.1109/DCC.2019.00012","url":null,"abstract":"In this paper, History-based Motion Vector Prediction (HMVP) is presented for video coding. With the proposed method, a table of HMVP candidates is maintained and updated on-the-fly. After decoding one inter-coded block, the table is updated by appending the associated motion information to the table as a new HMVP candidate. A First-In-First-Out (FIFO) rule is applied to manage the table. The HMVP candidates could be added to the Advanced Motion Vector Prediction (AMVP) candidate list as additional motion vector predictors. And they could also be added to the merge candidate list as additional merge candidates. With the proposed method, the motion information of previously coded blocks even not adjacent to the current block can be utilized for more efficient motion vector prediction. Simulation results have validated the efficiency of HMVP, wherein up to 4% BD rate saving could be achieved. The proposed method has been adopted by the next generation video coding standard, named Versatile Video Coding (VVC) developed by Joint Video Exploration Team (JVET).","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125325960","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
Machine Foveation: An Application-Aware Compressive Sensing Framework 机器注视:一个应用感知的压缩感知框架
2019 Data Compression Conference (DCC) Pub Date : 2019-03-01 DOI: 10.1109/DCC.2019.00056
E. S. Lubana, Vinayak Aggarwal, R. Dick
{"title":"Machine Foveation: An Application-Aware Compressive Sensing Framework","authors":"E. S. Lubana, Vinayak Aggarwal, R. Dick","doi":"10.1109/DCC.2019.00056","DOIUrl":"https://doi.org/10.1109/DCC.2019.00056","url":null,"abstract":"Embedded vision applications generally face tight resource constraints. Biological vision systems are optimized to operate under similar conditions; they use highly heterogeneous sensing patterns to capture only the most valuable information within scenes. Our exploration of similar approaches in embedded systems has led to the design of Machine Foveation–a general-purpose, application-aware compressive sensing related framework that uses a cascaded network architecture integrating an autoencoder and application network to determine the importance of each pixel. The cascaded structure results in inherent regulation of the autoencoder network, forcing it to learn a representation that retains a given feature only if it is crucial to the overall application. The framework further uses scene awareness for reducing the number of bits necessary to represent the image data. This reduces sensed data at minimal or no decrease in task accuracy and reduces signal communication latency and corresponding energy consumption in embedded systems. For example, when evaluated on the Fashion-MNIST data set, channel bandwidth requirements are reduced by 77.37% and signal communication latency is reduced by 64.6%, with an accuracy loss of only 0.32%.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128103983","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
Rate Allocation for Bayer-Pattern Image Compression with JPEG XS 基于JPEG XS的bayer模式图像压缩速率分配
2019 Data Compression Conference (DCC) Pub Date : 2019-03-01 DOI: 10.1109/DCC.2019.00040
T. Richter
{"title":"Rate Allocation for Bayer-Pattern Image Compression with JPEG XS","authors":"T. Richter","doi":"10.1109/DCC.2019.00040","DOIUrl":"https://doi.org/10.1109/DCC.2019.00040","url":null,"abstract":"Sensors in digital cameras use a technology called \"Bayer Patterns'\" allowing color photography with a planar arrangements of sensor elements. Alternating arrangements of red, green and blue filter masks on top of a rectangular grid of sensor elements allow capturing of color information, but also require a de-mosaicing algorithm to reconstruct a full-resolution color image from the sensor data. In high-speed applications, or applications where the system design requires low-latency, low-complexity compression, the JPEG~XS standard of the JPEG committee offers an elegant solution to compress Bayer pattern images close to the sensor, and to transmit the compressed data over a lower bandwidth connection while maintaining visually lossless quality. This paper discusses algorithms, and in particular modifications to the rate allocation of JPEG XS to enable compression of Bayer pattern sensor data. The main contribution of this work is a modified algorithm to compute band gains and band priorities that ensures PSNR-optimality of the demosaiced image rather the Bayer pattern itself.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121864730","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
Boosting Backward Search Throughput for FM-Index Using a Compressed Encoding 利用压缩编码提高FM-Index的向后搜索吞吐量
2019 Data Compression Conference (DCC) Pub Date : 2019-03-01 DOI: 10.1109/DCC.2019.00089
Jose M. Herruzo, S. González-Navarro, P. Ibáñez, V. Viñals, Jesús Alastruey-Benedé, O. Plata
{"title":"Boosting Backward Search Throughput for FM-Index Using a Compressed Encoding","authors":"Jose M. Herruzo, S. González-Navarro, P. Ibáñez, V. Viñals, Jesús Alastruey-Benedé, O. Plata","doi":"10.1109/DCC.2019.00089","DOIUrl":"https://doi.org/10.1109/DCC.2019.00089","url":null,"abstract":"The rapid development of DNA sequencing technologies has demanded for compressed data structures supporting fast pattern matching queries. FM-index is a widely-used compressed data structure that also supports fast pattern matching queries. It is common for the exact matching algorithm to be memory bound, resulting in poor performance. We propose a new data-layout of FM-index that compacts all data needed to perform the searching process. This results in an improvement of the search computing time for genomic data.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131321527","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
Integer Fresnel Transform for Lossless Hologram Compression 无损全息图压缩的整数菲涅耳变换
2019 Data Compression Conference (DCC) Pub Date : 2019-03-01 DOI: 10.1109/DCC.2019.00047
David Blinder, P. Schelkens
{"title":"Integer Fresnel Transform for Lossless Hologram Compression","authors":"David Blinder, P. Schelkens","doi":"10.1109/DCC.2019.00047","DOIUrl":"https://doi.org/10.1109/DCC.2019.00047","url":null,"abstract":"Digital holograms fully encode the wavefield of light, thereby having many applications both for 3D object measurements as well as for display purposes, accounting for all human visual cues. Because the statistics and properties of holographic signals differ considerably from natural imagery such as photographs, conventional coding solutions will be sub-optimal. In this paper, we propose the integer Fresnel transform, which is – to our knowledge – the first lossless transform tailored for hologram coding. By combining the proposed transform with JPEG 2000, we report bit-rate savings from 0.12 up to 2.83 bits per channel on a collection of 8 digital holograms obtained from 3 different databases.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131974741","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
Level-of-Detail Generation Using Binary-Tree for Lifting Scheme in LiDAR Point Cloud Attributes Coding 激光雷达点云属性编码中基于二叉树提升方案的细节级生成
2019 Data Compression Conference (DCC) Pub Date : 2019-03-01 DOI: 10.1109/DCC.2019.00092
B. Kathariya, Vladyslav Zakharchenko, Zhu Li, Jianle Chen
{"title":"Level-of-Detail Generation Using Binary-Tree for Lifting Scheme in LiDAR Point Cloud Attributes Coding","authors":"B. Kathariya, Vladyslav Zakharchenko, Zhu Li, Jianle Chen","doi":"10.1109/DCC.2019.00092","DOIUrl":"https://doi.org/10.1109/DCC.2019.00092","url":null,"abstract":"Point clouds are one of the emerging 3D visual representations of real word and plenty of useful applications has already been demonstrated. However, a huge amount of data associated with it has added challenges in both transmission and storage. This requires an efficient coding solution and brought a great attention among compression community. MPEG and JPEG standardization group has already started developing coding solution and proposed two test-models namely V-PCC, video-based coding solution, for dynamic point cloud and G-PCC, a native geometry-based coding solution, for static and LiDAR point cloud. In G-PCC, octree (lossless) and tri-soup(lossy) for geometry coding, similarly regional adaptive hierarchical transform (RAHT) and lifting-scheme for attributes coding are currently being explored. Lifting-scheme relies on level-of-details(LOD) structure for attributes prediction where LOD is generated with distance based subsampling approach. In this work we proposed a new LOD generation scheme using binary-tree and showed it provides better coding solution for sparse point cloud such as LiDAR. The experimental results demonstrated 12% bitrate reduction for reflectance and 8%, 6% and 7% bitrate reduction for luma, chroma Cb and chroma Cr respectively as well as up to 4 times computational complexity reduction compared to current G-PCC lifting-scheme.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132481626","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: 4
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