2019 Data Compression Conference (DCC)最新文献_第4页

ResGAN: A Low-Level Image Processing Network to Restore Original Quality of JPEG Compressed Images ResGAN:一种低级图像处理网络，用于恢复JPEG压缩图像的原始质量

2019 Data Compression Conference (DCC) Pub Date : 2019-03-26 DOI: 10.1109/DCC.2019.00128

Chunbiao Zhu, Yuanqi Chen, Yiwei Zhang, Shan Liu, Ge Li

引用次数: 4

Fast CU Size Decision Based on AQ-CNN for Depth Intra Coding in 3D-HEVC 基于AQ-CNN的3D-HEVC深度内编码快速CU大小决策

2019 Data Compression Conference (DCC) Pub Date : 2019-03-26 DOI: 10.1109/DCC.2019.00073

Yamei Chen, Li Yu, Tiansong Li, Hongkui Wang, Shengwei Wang

引用次数: 4

A New Technique for Lossless Compression of Color Images Based on Hierarchical Prediction, Inversion and Context Adaptive Coding 基于分层预测、反演和上下文自适应编码的彩色图像无损压缩新技术

2019 Data Compression Conference (DCC) Pub Date : 2019-03-26 DOI: 10.1109/DCC.2019.00096

B. Koc, Z. Arnavut, D. Sarkar, H. Kocak

引用次数: 1

Adaptive Quantization Parameter Selection Leveraging the Inter-Frame Distortion Propagation for HEVC Video Coding 利用帧间失真传播的HEVC视频编码自适应量化参数选择

2019 Data Compression Conference (DCC) Pub Date : 2019-03-26 DOI: 10.1109/DCC.2019.00100

Dong Li, Haibing Yin, Xiaofeng Huang, Hang Li

引用次数: 2

A Multi-Pass Coding Mode Search Framework For AV1 Encoder Optimization AV1编码器优化的多通道编码模式搜索框架

2019 Data Compression Conference (DCC) Pub Date : 2019-03-26 DOI: 10.1109/DCC.2019.00054

Ching-Han Chiang, Jingning Han, Yaowu Xu

{"title":"A Multi-Pass Coding Mode Search Framework For AV1 Encoder Optimization","authors":"Ching-Han Chiang, Jingning Han, Yaowu Xu","doi":"10.1109/DCC.2019.00054","DOIUrl":"https://doi.org/10.1109/DCC.2019.00054","url":null,"abstract":"The AV1 codec recently released by the Alliance of Open Media provides nearly 30% BDrate reduction over its predecessor VP9. It substantially extends the available coding block sizes and supports a wide range of prediction modes. There are also a large variety of transform kernel types and sizes. The combination provides an extremely wide range of flexible coding options. To translate such flexibility into compression efficiency, the encoder needs to conduct an extensive search over the space of coding modes. Optimization of the encoder complexity and compression efficiency trade-off is critical to productionizing AV1. Many research efforts have been devoted to devising feature space based pruning methods ranging from decision rules based on some simple observations to more complex neural network models. A multi-pass coding mode search framework is proposed in this work to provide a structural approach to reduce the search volume. It decomposes the original high dimensional space search into cascaded stages of lower dimensional space searches. To retain a near optimal search result, the scheme departs from conventional dimension reduction approach in which one retains a single winner at each stage, and uses that winner for the next stage (dimension). Instead, this framework retains a subset of the states that are the most likely winners at each stage, which are then fed into the next stage to find the next subset of winners. The subset size at each stage is determined by the likelihood that the optimal route will be captured in the current stage. Changing this likelihood parameter tunes the encoder for speed and compression performance trade-off. This framework can integrate with most existing feature based methods at its various stages. The framework provides 60% encoding time reduction at the expense of 0.6% compression loss in libaom AV1 encoder.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117319886","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: 5

An Overview of the OMAF Standard for 360° Video 360°视频OMAF标准概述

2019 Data Compression Conference (DCC) Pub Date : 2019-03-26 DOI: 10.1109/DCC.2019.00050

M. Hannuksela, Ye-Kui Wang, Ari Hourunranta

引用次数: 21

Rate Control Algorithm in HEVC Based on Scene-Change Detection 基于场景变化检测的HEVC速率控制算法

2019 Data Compression Conference (DCC) Pub Date : 2019-03-26 DOI: 10.1109/DCC.2019.00112

Jia Qin, H. Bai, Yao Zhao

引用次数: 0

Dynamic Lists for Efficient Coding of Intra Prediction Modes in the Future Video Coding Standard 未来视频编码标准中有效编码帧内预测模式的动态列表

2019 Data Compression Conference (DCC) Pub Date : 2019-03-26 DOI: 10.1109/DCC.2019.00113

Kevin Reuzé, W. Hamidouche, P. Philippe, O. Déforges

{"title":"Dynamic Lists for Efficient Coding of Intra Prediction Modes in the Future Video Coding Standard","authors":"Kevin Reuzé, W. Hamidouche, P. Philippe, O. Déforges","doi":"10.1109/DCC.2019.00113","DOIUrl":"https://doi.org/10.1109/DCC.2019.00113","url":null,"abstract":"The next generation MPEG video coding standard is under development by the Joint Video Coding Experts Team (JVET). This new standard, called Versatile Video Coding (VVC), is expected by the end of 2020 and will offer better coding efficiency than its predecessor High Efficiency Video Coding (HEVC) standard. This coding gain is enabled by new coding tools such as more flexible block partitioning, more accurate Intra/Inter predictions, multiple transforms and adaptive in-loop filtering. In this paper we focus on the coding of the Intra Prediction Modes (IPM) that have been increased from 35 modes in HEVC to 67 modes in VVC. We propose a solution based on genetic algorithms to build an ordered list for the coding of IPM in the Joint Exploration Model (JEM) codec. We first give the theoretical upper bound performance in terms of required bits per IPM to encode the IPM using the available contextual information. The new ordering of the labels associated with more efficient codes is then proposed to efficiently leverage contextual informations available in the encoder and construct the Most Probable Modes (MPM) list. The proposed coding scheme enables to increase the BD-BR performance in average by 0.09% for the same level of complexity compared to the JEM.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127460851","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

Fast Early Termination of CU Partition and Mode Selection Algorithm for Virtual Reality Video in HEVC HEVC虚拟现实视频中CU分区的快速早期终止及模式选择算法

2019 Data Compression Conference (DCC) Pub Date : 2019-03-26 DOI: 10.1109/DCC.2019.00088

Xiaohan Guan, Xiaosha Dong, Mengmeng Zhang, Zhi Liu

引用次数: 4

Dataflow-Based Joint Quantization for Deep Neural Networks 基于数据流的深度神经网络联合量化

2019 Data Compression Conference (DCC) Pub Date : 2019-03-26 DOI: 10.1109/DCC.2019.00086

Xue Geng, Jie Fu, Bin Zhao, Jie Lin, M. Aly, C. Pal, V. Chandrasekhar

{"title":"Dataflow-Based Joint Quantization for Deep Neural Networks","authors":"Xue Geng, Jie Fu, Bin Zhao, Jie Lin, M. Aly, C. Pal, V. Chandrasekhar","doi":"10.1109/DCC.2019.00086","DOIUrl":"https://doi.org/10.1109/DCC.2019.00086","url":null,"abstract":"This paper addresses a challenging problem – how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage. In order to alleviate the computation and storage burdens, we propose a novel dataflow-based joint quantization approach with the hypothesis that a fewer number of quantization operations would incur less information loss and thus improve the final performance. It first introduces a quantization scheme with efficient bit-shifting and rounding operations to represent network parameters and activations in low precision. Then it re-structures the network architectures to form unified modules for optimization on the quantized model. Extensive experiments on ImageNet and KITTI validate the effectiveness of our model, demonstrating that state-of-the-art results for various tasks can be achieved by this quantized model. Besides, we designed and synthesized an RTL model to measure the hardware costs among various quantization methods. For each quantization operation, it reduces area cost by about 15 times and energy consumption by about 9 times, compared to a strong baseline.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124602952","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