LVT: A Learned Video Transcoding Framework

Abstract

With the exponential growth of video traffic and the continuous evolution of video coding standards, video transcoding has become essential for existing bitstreams to benefit from the advanced features of new video compression technologies. Typically, video transcoding involves decoding an existing bitstream and re-encoding the decoded sequence into a target format. A key challenge in transcoding is the inevitable presence of compression artifacts in the decoded sequences, which, if not properly addressed, can degrade transcoding efficiency by causing suboptimal bit allocation and disrupting core coding processes. In this article, a learned video transcoding framework (LVT) is proposed to optimize video transcoding, leveraging coding priors from the input bitstream to guide the transcoding process. In the framework, to mitigate the adverse effects of compression artifacts, a Coding Priors-Guided Spatial Feature Transform module is designed, which utilizes coding prior features to adaptively modulate intermediate features through spatial affine transformations, enhancing bit allocation and suppressing artifacts. Additionally, a Coding Priors-Guided Quality Adapter module is proposed to generate a compression degradation representation using coding priors, which dynamically interacts with intermediate features to enable the network to perceive and adapt to different levels of degradation in the input video. Furthermore, a Motion Vectors-Guided Flow Refinement module is proposed to reduce prediction errors caused by artifacts. It refines optical flow predictions by using motion vectors from the bitstream as auxiliary information. Extensive experiments demonstrate that our framework outperforms both existing traditional and learned video codecs in transcoding performance, achieving an average bitrate saving of 20.3% compared to the H.266/VVC reference software VTM under the practical YUV420 setting measured with PSNR.