2018 Data Compression Conference最新文献

Fixed-Rate Zero-Delay Source Coding for Stationary Vector-Valued Gauss-Markov Sources 平稳向量值高斯-马尔可夫信源的固定速率零延迟编码

2018 Data Compression Conference Pub Date : 2018-07-19 DOI: 10.1109/DCC.2018.00034

Photios A. Stavrou, Jan Østergaard

引用次数: 6

The Multi-Scale Deep Decoder for the Standard HEVC Bitstreams 标准HEVC码流的多尺度深度解码器

2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00028

Tingting Wang, Wenhui Xiao, Mingjin Chen, Hongyang Chao

{"title":"The Multi-Scale Deep Decoder for the Standard HEVC Bitstreams","authors":"Tingting Wang, Wenhui Xiao, Mingjin Chen, Hongyang Chao","doi":"10.1109/DCC.2018.00028","DOIUrl":"https://doi.org/10.1109/DCC.2018.00028","url":null,"abstract":"As we all know, there is strong multi-scale similarity among video frames. However, almost none of the current video coding standards takes this similarity into consideration. There exist two major problems when utilizing the multi-scale information at encoder-end: one is the extra motion models and the overheads brought by new motion parameters; the other is the extreme increment of the encoding algorithms’ complexity. Is it possible to employ the multi-scale similarity only at the decoder-end to improve the decoded videos’ quality, i.e., to further boost the coding efficiency? This paper mainly studies how to answer this question by proposing a novel Multi-Scale Deep Decoder (MSDD) for HEVC. Benefiting from the efficiency of deep learning technology (Convolutional Neural Network and Long Short-Term Memory network), MSDD achieves a higher coding efficiency only at the decoder-end without changing any encoding algorithms. Extensive experiments validate the feasibility and effectiveness of MSDD. MSDD leads to on averagely 6.5%, 8.0%, 6.4%, and 6.7% BD-rate reduction compared to HEVC anchor, for AI, LP, LB and RA coding configurations respectively. Especially for the videos with multi-scale similarity, the proposed approach obviously improves the coding efficiency indeed.","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":"114986169","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: 12

Engineering Compressed Static Functions 工程压缩静态函数

2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00013

M. Genuzio, S. Vigna

{"title":"Engineering Compressed Static Functions","authors":"M. Genuzio, S. Vigna","doi":"10.1109/DCC.2018.00013","DOIUrl":"https://doi.org/10.1109/DCC.2018.00013","url":null,"abstract":"Recent advances in the compact representation of static functions (with constant access time) have made it possible to fully exploit constructions based on random linear system. Such constructions, albeit theoretically appealing, were previously too slow to be usable. In this paper, we extend such techniques to the problem of storing compressed static functions, in the sense that the space used per key should be close to the entropy of the list of values. From a theoretical viewpoint, we are inspired by the approach of Hreinsson, Krøyer and Pagh. Values are represented using a near-optimal instantaneous code. Then, a bit array is created so that by XOR’ing its content at a fixed number of positions depending on the key one obtains the value, represented by its associated codeword. In the construction phase, every bit of the array is associated with an equation on Z/2Z, and solving the associated system provides the desired representation. Thus, we pass from one equation per key (the non-compressed case) to one equation per bit: the size of the system is thus approximately multiplied by the empirical entropy of the values, making the problem much more challenging. We show that by carefully engineering the value representation we can obtain a practical data structure. For example, we can store a function with geometrically distributed output in just 2.28 bits per key, independently of the key set, with a construction time double with respect to that of a state-of-the-art non-compressed function, which requires ≈ log log n bits per key, where n is the number of keys, and slightly improved lookup time. We can also store a function with an output of 106 values distributed following a power law of exponent 2 in just 2.75 bits per key, whereas a non-compressed function would require more than 20, with a threefold increase in construction time and significantly faster lookups.","PeriodicalId":137206,"journal":{"name":"2018 Data Compression Conference","volume":"118 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":"115597310","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 Motion Compensated JPEG2000 运动补偿JPEG2000的速率分配

2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00015

Jose Carmelo Maturana-Espinosa, V. Ruiz, J. Ortiz, D. Muller

引用次数: 0

Detail-Aware Image Decomposition for an HEVC-Based Texture Synthesis Framework 基于hevc纹理合成框架的细节感知图像分解

2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00083

Bastian Wandt, Thorsten Laude, B. Rosenhahn, J. Ostermann

{"title":"Detail-Aware Image Decomposition for an HEVC-Based Texture Synthesis Framework","authors":"Bastian Wandt, Thorsten Laude, B. Rosenhahn, J. Ostermann","doi":"10.1109/DCC.2018.00083","DOIUrl":"https://doi.org/10.1109/DCC.2018.00083","url":null,"abstract":"Modern video coding standards like High Efficiency Video Coding (HEVC) provide superior coding efficiency. However, this does not state true for complex and hard to predict textures which require high bit rates to achieve a high quality. To overcome this limitation of HEVC, texture synthesis frameworks were proposed in previous works. However, these frameworks only result in good reconstruction quality if the decomposition into synthesizable and non-synthesizable regions is either known or trivial. The frameworks fail for more challenging content, e.g. for content with fine non-synthesizable details within synthesizable regions. To enable texture synthesis-based video coding with high quality for this content, we propose sophisticated detail-aware decomposition techniques in this paper. These techniques are based on an initial coarse segmentation step followed by a refinement step that detects even small differences in the previously segmented region. With this new approach, we are able to achieve average luma BD-rate gains of 13.77% over HEVC and 3.03% over the closest related work from the literature. Furthermore, the considerably improved visual quality in addition to the bit rate savings is confirmed by comprehensive subjective tests.","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":"125367440","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 Algorithm for HEVC Intra Prediction Based on Adaptive Mode Decision and Early Termination of CU Partition 基于自适应模式决策和CU分区早期终止的HEVC内快速预测算法

2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00087

Mengmeng Zhang, Xiaojun Zhai, Zhi Liu, Changzhi An

引用次数: 3

High Efficient Snake Order Pseudo-Sequence Based Light Field Image Compression 基于蛇阶伪序列的高效光场图像压缩

2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00050

Hadi Amirpour, Manuela Pereira, A. Pinheiro

引用次数: 22

Constant Delay Traversal of Compressed Graphs 压缩图的恒延迟遍历

2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00011

S. Maneth, Fabian Peternek

引用次数: 1

Efficient Processing of top-K Vector-Raster Queries Over Compressed Data 压缩数据上top-K矢量栅格查询的高效处理

2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00063

Gilberto Gutiérrez, Susana Ladra, Juan R. Lopez, J. Paramá, Fernando Silva-Coira

引用次数: 1

Optimal Single- and Multiple-Tree Almost Instantaneous Variable-to-Fixed Codes 最优单树和多树几乎瞬时变到固定码

2018 Data Compression Conference Pub Date : 2018-03-27 DOI: 10.1109/DCC.2018.00058

Danny Dubé, Fatma Haddad

引用次数: 2