论文标题
基于CNN的后处理器，用于感知优化的沉浸式媒体压缩
A CNN-based Post-Processor for Perceptually-Optimized Immersive Media Compression
论文作者
论文摘要
近年来，基于深层神经网络的分辨率改编已使常规（2D）视频编解码器的大量绩效提高。本文研究了在沉浸式含量的背景下空间分辨率分辨采样的有效性。提出的方法在编码之前减少了输入多视频视频的空间分辨率，并在解码后重建其原始分辨率。在上采样过程中，使用高级CNN模型来减少潜在的重新采样，压缩和合成伪像。使用多功能视频编码（VVC）编解码器对TMIV编码标准进行了全面测试。结果表明，所提出的方法可在大多数测试序列中取得显着的速率质量性能提高，而平均BD-VMAF提高了3.07总序列。
In recent years, resolution adaptation based on deep neural networks has enabled significant performance gains for conventional (2D) video codecs. This paper investigates the effectiveness of spatial resolution resampling in the context of immersive content. The proposed approach reduces the spatial resolution of input multi-view videos before encoding, and reconstructs their original resolution after decoding. During the up-sampling process, an advanced CNN model is used to reduce potential re-sampling, compression, and synthesis artifacts. This work has been fully tested with the TMIV coding standard using a Versatile Video Coding (VVC) codec. The results demonstrate that the proposed method achieves a significant rate-quality performance improvement for the majority of the test sequences, with an average BD-VMAF improvement of 3.07 overall sequences.
