Low-complexity content-aware encoding optimization of batch video

Abstract

With the proliferation of short-form video traffic, video service providers are faced with the challenge of balancing video quality and bandwidth consumption while processing massive volumes of videos. The most straightforward and simplistic approach is to set uniformly encoding parameters to all videos. However, such an approach fails to consider the differences in video content, and there may be alternative encoding parameter configuration approach that can improve global coding efficiency. Finding the optimal combination of encoding parameter configurations for a batch of videos requires an amount of redundant encoding, thereby introducing significant computational costs. To address this issue, we propose a low-complexity encoding parameter prediction model that can adaptively adjust the values of the encoding parameters based on video content. The experiments show that when only changing the value of the encoding parameter CRF, our prediction model can achieve 27.04%, 6.11%, and 15.92% bit saving in terms of PSNR, SSIM, and VMAF respectively, while maintaining an acceptable complexity compared to the approach using the same CRF value.