Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation

TL;DR

This paper introduces HRLVSC, a hierarchical reinforcement learning approach for video semantic coding that simplifies mode decision and significantly improves compression efficiency for segmentation tasks.

Contribution

It proposes a hierarchical RL framework for video semantic coding, including mode decision simplification and cooperative frame-level and CTU-level agents.

Findings

Achieves over 39% BD-rate saving in video semantic coding.

Effectively simplifies complex mode decision in video semantic compression.

Validates approach on HEVC with significant performance improvements.

Abstract

The rapid development of intelligent tasks, e.g., segmentation, detection, classification, etc, has brought an urgent need for semantic compression, which aims to reduce the compression cost while maintaining the original semantic information. However, it is impractical to directly integrate the semantic metric into the traditional codecs since they cannot be optimized in an end-to-end manner. To solve this problem, some pioneering works have applied reinforcement learning to implement image-wise semantic compression. Nevertheless, video semantic compression has not been explored since its complex reference architectures and compression modes. In this paper, we take a step forward to video semantic compression and propose the Hierarchical Reinforcement Learning based task-driven Video Semantic Coding, named as HRLVSC. Specifically, to simplify the complex mode decision of video semantic coding, we divided the action space into frame-level and CTU-level spaces in a hierarchical manner, and then explore the best mode selection for them progressively with the cooperation of frame-level and CTU-level agents. Moreover, since the modes of video semantic coding will exponentially increase with the number of frames in a Group of Pictures (GOP), we carefully investigate the effects of different mode selections for video semantic coding and design a simple but effective mode simplification strategy for it. We have validated our HRLVSC on the video segmentation task with HEVC reference software HM16.19. Extensive experimental results demonstrated that our HRLVSC can achieve over 39% BD-rate saving for video semantic coding under the Low Delay P configuration.