Adaptive GOP Size Decision for Multi-Pass Video Coding Based on Hidden Markov Model

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.