论文标题
通过基于CNN的有效位深度适应来增强HDR视频压缩
Enhancing HDR Video Compression through CNN-based Effective Bit Depth Adaptation
论文作者
论文摘要
众所周知，与传统的标准动态范围内容相比，高动态范围（HDR）视频可以提供更多的沉浸式视觉体验。但是，由于与更广泛的动态范围相关的细节增加，HDR内容通常更具挑战性编码。在本文中，我们使用有效的位深度适应方法（EBDA）提高了HDR压缩性能。此方法在编码并使用基于CNN的基于CNN的上采样方法来重建原始视频内容的有效位深度。在这项工作中，我们修改了MFRNET网络体系结构以实现多个帧处理，并且使用两个通用视频编码（VVC）主机编码器：VTM 16.2和Fraunhofer Versatile Versatile Video Expoder（Vvenc 1.4.0），使用两个通用视频编码（VVC）主机编码器：VTM 16.2和FRAUNHOFER EXPORECS：VTM 16.2。使用随机访问配置在JVET HDR公共测试条件下评估了所提出的方法。结果表明，在JVET HDR测试序列上，原始VVC VTM 16.2和VVENC 1.4.0（w/o eBDA）上的编码增长，基于Bjontegaard Delta的测量，平均比特率节省了2.9％（超过VTM）和4.8％（对VVEN）。多帧MFRNET的源代码已在https://github.com/fan-aaron-zhang/mf-mfrnet上发布。
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.
