Bitrate Estimation for Spatial Scalable Videos

作     者：Jiang, Zhuqing Wei, Likuo Zeng, Ganmin Qi, Shuwen Wang, Haiying Men, Aidong Zhou, Yun 

作者机构：Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Key Lab Network Syst & Network Culture Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Acad Broadcasting Sci TV Technol Res Inst Beijing 100866 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON BROADCASTING》 (IEEE广播汇刊)

年 卷 期：2021年第67卷第2期

页      面：549-555页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 

基　　金：National Key Research and Development Program [2018YFB0505200] National Natural Science Funding MoE-CMCC "Artificial Intelligence" Project [MCM20190701] 

主　　题：Bit rate Videos Estimation Encoding Resource management Complexity theory Video coding SHVC bitrate estimation multi-linear regression 

摘      要：Spatial scalable video service has surged in the time of multiple screens. Existing bitrate allocation methods are principled by rate-distortion theory and characterized by iterative encoding, which is accurate yet complex. However, the quasi-quantitative description is preferred in practice of broadcasting. In this paper, we propose a task of bitrate estimation for scalable videos concerning the content, aiming at a more efficient model at the cost of precision. First, we exhibit necessity to build a model for Scalable High Efficiency Video Coding (SHVC) and quantitative relation between video content and bitrate using different encoders. Then, a scalable-video dataset is prepared. It covers various types of content to offer diversity for model training. In the end, multi-linear regression is utilized to estimate the bitrate of scalable videos, with spatial and temporal indices as explanatory variables. Our statistical experiments show the model is able to estimate bitrate after trained on the self-built dataset.