Enhancing HDR Video Compression through CNN-based Effective Bit Depth Adaptation

TL;DR

This paper introduces a CNN-based effective bit depth adaptation method for HDR video compression, reducing bitrate while maintaining quality, by modifying a multi-frame network integrated into existing codecs.

Contribution

It presents a novel multi-frame MFRNet architecture for effective bit depth adaptation in HDR video compression, integrated with VVC codecs for improved coding efficiency.

Findings

Achieved 2.9% bitrate savings over VTM 16.2

Achieved 4.8% bitrate savings over VVenC 1.4.0

Demonstrated improved compression performance on JVET HDR sequences

Abstract

It is well known that high dynamic range (HDR) video can provide more immersive visual experiences compared to conventional standard dynamic range content. However, HDR content is typically more challenging to encode due to the increased detail associated with the wider dynamic range. In this paper, we improve HDR compression performance using the effective bit depth adaptation approach (EBDA). This method reduces the effective bit depth of the original video content before encoding and reconstructs the full bit depth using a CNN-based up-sampling method at the decoder. In this work, we modify the MFRNet network architecture to enable multiple frame processing, and the new network, multi-frame MFRNet, has been integrated into the EBDA framework using two Versatile Video Coding (VVC) host codecs: VTM 16.2 and the Fraunhofer Versatile Video Encoder (VVenC 1.4.0). The proposed approach was evaluated under the JVET HDR Common Test Conditions using the Random Access configuration. The results show coding gains over both the original VVC VTM 16.2 and VVenC 1.4.0 (w/o EBDA) on JVET HDR tested sequences, with average bitrate savings of 2.9% (over VTM) and 4.8% (against VVenC) based on the Bjontegaard Delta measurement. The source code of multi-frame MFRNet has been released at https://github.com/fan-aaron-zhang/MF-MFRNet.