2015 Data Compression Conference最新文献_第3页

On the Efficiency of View Synthesis Prediction for 3D Video Coding 三维视频编码中视点综合预测效率的研究

2015 Data Compression Conference Pub Date : 2015-04-07 DOI: 10.1109/DCC.2015.44

Yichen Zhang, Ngai-Man Cheung, Lu Yu

引用次数: 1

Compressing Yahoo Mail 压缩Yahoo Mail

2015 Data Compression Conference Pub Date : 2015-04-07 DOI: 10.1109/DCC.2015.15

Aran Bergman, Eyal Zohar

引用次数: 2

Strategy of Microscopic Parallelism for Bitplane Image Coding 位平面图像编码的微观并行策略

2015 Data Compression Conference Pub Date : 2015-04-07 DOI: 10.1109/DCC.2015.19

F. Aulí-Llinàs, P. Enfedaque, J. Moure, Ian Blanes, Victor Sanchez

{"title":"Strategy of Microscopic Parallelism for Bitplane Image Coding","authors":"F. Aulí-Llinàs, P. Enfedaque, J. Moure, Ian Blanes, Victor Sanchez","doi":"10.1109/DCC.2015.19","DOIUrl":"https://doi.org/10.1109/DCC.2015.19","url":null,"abstract":"Recent years have seen the upraising of a new type of processors strongly relying on the Single Instruction, Multiple Data (SIMD) architectural principle. The main idea behind SIMD computing is to apply a flow of instructions to multiple pieces of data in parallel and synchronously. This permits the execution of thousands of operations in parallel, achieving higher computational performance than with traditional Multiple Instruction, Multiple Data (MIMD) architectures. The level of parallelism required in SIMD computing can only be achieved in image coding systems via microscopic parallel strategies that code multiple coefficients in parallel. Until now, the only way to achieve microscopic parallelism in bit plane coding engines was by executing multiple coding passes in parallel. Such a strategy does not suit well SIMD computing because each thread executes different instructions. This paper introduces the first bit plane coding engine devised for the fine grain of parallelism required in SIMD computing. Its main insight is to allow parallel coefficient processing in a coding pass. Experimental tests show coding performance results similar to those of JPEG2000.","PeriodicalId":313156,"journal":{"name":"2015 Data Compression Conference","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115455946","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

Cuboid Coding of Depth Motion Vectors Using Binary Tree Based Decomposition 基于二叉树分解的深度运动向量长方体编码

2015 Data Compression Conference Pub Date : 2015-04-07 DOI: 10.1109/DCC.2015.43

Shampa Shahriyar, M. Murshed, Mortuza Ali, M. Paul

{"title":"Cuboid Coding of Depth Motion Vectors Using Binary Tree Based Decomposition","authors":"Shampa Shahriyar, M. Murshed, Mortuza Ali, M. Paul","doi":"10.1109/DCC.2015.43","DOIUrl":"https://doi.org/10.1109/DCC.2015.43","url":null,"abstract":"Motion vectors of depth-maps in multiview and free-viewpoint videos exhibit strong spatial as well as inter-component clustering tendency. This paper presents a novel motion vector coding technique that first compresses the multidimensional bitmaps of macro block mode information and then encodes only the non-zero components of motion vectors. The bitmaps are partitioned into disjoint cuboids using binary tree based decomposition so that the 0's and 1's are either highly polarized or further sub-partitioning is unlikely to achieve any compression. Each cuboid is entropy-coded as a unit using binary arithmetic coding. This technique is capable of exploiting the spatial and inter-component correlations efficiently without the restriction of scanning the bitmap in any specific linear order as needed by run-length coding. As encoding of non-zero component values no longer requires denoting the zero value, further compression efficiency is achieved. Experimental results on standard multiview test video sequences have comprehensively demonstrated the superiority of the proposed technique, achieving overall coding gain against the state-of-the-art in the range [17%,51%] and on average 31%.","PeriodicalId":313156,"journal":{"name":"2015 Data Compression Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124951425","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

Hybrid Image Compression by Using Vector Quantization (VQ) and Vector-Embedded Karhunen-Loève Transform (VEKLT) 矢量量化(VQ)和矢量嵌入karhunen - lo<e:1>变换(VEKLT)混合图像压缩

2015 Data Compression Conference Pub Date : 2015-04-07 DOI: 10.1109/DCC.2015.14

Kiung Park

引用次数: 1

Geometric Compression of Orientation Signals for Fast Gesture Analysis 快速手势分析中方向信号的几何压缩

2015 Data Compression Conference Pub Date : 2015-04-07 DOI: 10.1109/DCC.2015.39

A. Sivakumar, Rushil Anirudh, P. Turaga

引用次数: 0

2-D Index Map Coding for HEVC Screen Content Compression 用于HEVC屏幕内容压缩的二维索引地图编码

2015 Data Compression Conference Pub Date : 2015-04-07 DOI: 10.1109/DCC.2015.62

Yiling Xu, Wei Huang, Wei Wang, Fanyi Duanmu, Zhan Ma

引用次数: 6

Resampling Process of the Scalable High Efficiency Video Coding 可扩展高效视频编码的重采样过程

2015 Data Compression Conference Pub Date : 2015-04-07 DOI: 10.1109/DCC.2015.60

Jianle Chen, E. Alshina, Xiang Li, M. Karczewicz, A. Alshin

引用次数: 0

Block-Based Compressive Sensing Coding of Natural Images by Local Structural Measurement Matrix 基于局部结构测量矩阵的自然图像分块压缩感知编码

2015 Data Compression Conference Pub Date : 2015-04-07 DOI: 10.1109/DCC.2015.47

Xinwei Gao, Jian Zhang, Wenbin Che, Xiaopeng Fan, Debin Zhao

{"title":"Block-Based Compressive Sensing Coding of Natural Images by Local Structural Measurement Matrix","authors":"Xinwei Gao, Jian Zhang, Wenbin Che, Xiaopeng Fan, Debin Zhao","doi":"10.1109/DCC.2015.47","DOIUrl":"https://doi.org/10.1109/DCC.2015.47","url":null,"abstract":"Gaussian random matrix (GRM) has been widely used to generate linear measurements in compressive sensing (CS) of natural images. However, in practice, there actually exist two problems with GRM. One is that GRM is non-sparse and complicated, leading to high computational complexity and high difficulty in hardware implementation. The other is that regardless of the characteristics of signal the measurements generated by GRM are also random, which results in low efficiency of compression coding. In this paper, we design a novel local structural measurement matrix (LSMM) for block-based CS coding of natural images by utilizing the local smooth property of images. The proposed LSMM has two main advantages. First, LSMM is a highly sparse matrix, which can be easily implemented in hardware, and its reconstruction performance is even superior to GRM at low CS sampling sub rate. Second, the adjacent measurement elements generated by LSMM have high correlation, which can be exploited to greatly improve the coding efficiency. Furthermore, this paper presents a new framework with LSMM for block-based CS coding of natural images, including measurement generating, measurement coding and CS reconstruction. Experimental results show that the proposed framework with LSMM for block-based CS coding of natural images greatly enhances the existing CS coding performance when compared with other state-of-the-art image CS coding schemes.","PeriodicalId":313156,"journal":{"name":"2015 Data Compression Conference","volume":"278 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134486294","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: 47

Lossless Data Compression via Substring Enumeration for k-th Order Markov Sources with a Finite Alphabet 基于子串枚举的有限字母k阶马尔可夫源无损数据压缩

2015 Data Compression Conference Pub Date : 2015-04-07 DOI: 10.1109/DCC.2015.51

K. Iwata, M. Arimura

引用次数: 3