Most video platforms provide video streaming services with different
qualities, and the quality of the services is usually adjusted by the
resolution of the videos. So high-resolution videos need to be downsampled for
compression. In order to solve the problem of video coding at different
resolutions, we propose a rate-guided arbitrary rescaling network (RARN) for
video resizing before encoding. To help the RARN be compatible with standard
codecs and generate compression-friendly results, an iteratively optimized
transformer-based virtual codec (TVC) is introduced to simulate the key
components of video encoding and perform bitrate estimation. By iteratively
training the TVC and the RARN, we achieved 5%-29% BD-Rate reduction anchored by
linear interpolation under different encoding configurations and resolutions,
exceeding the previous methods on most test videos. Furthermore, the
lightweight RARN structure can process FHD (1080p) content at real-time speed
(91 FPS) and obtain a considerable rate reduction.

本文介绍了一种基于率导向任意重缩放网络 (RARN) 的视频缩放算法，通过迭代优化编码器 (Transformer-based Virtual Codec, TVC) 和 RARN 结构，在不同编码配置和分辨率下实现了 5％-29％BD-Rate 降低，超过了以前的方法。此外，本算法结构轻便，能够以实时速度处理 FHD（1080p）内容并获得相当的费率降低。