2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-4.1
Paper Title ADAPTIVE GOP SIZE DECISION FOR MULTI-PASS VIDEO CODING BASED ON HIDDEN MARKOV MODEL
Authors Bohan Li, Jingning Han, Yaowu Xu, Google LLC, United States
SessionIVMSP-4: Image & Video Coding 2
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVCOM] Image & Video Communications
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Multi-pass coding is a widely utilized technique to improve the compression efficiency in video coding, where frame statistics are collected from the previous passes and then analyzed to provide better encoder decisions, such as rate control parameters, prediction mode selection, motion estimation, etc. In this paper, a novel method to determine the size of each group of picture (GOP) using the multi-pass information is presented. In particular, we propose to categorize frames into regions with different natures, including stationary, high-variance, blending, and scene cut, through analyzing the frame statistics generated from the previous passes using a hidden Markov model. The GOP size is then determined based on the region types and the inter frame correlations. It is experimentally shown that the proposed adaptive GOP size decision provides considerable coding performance improvements over conventional fixed GOP length.