Bitrate Ladder Construction using Visual Information Fidelity

TL;DR

This paper introduces a method for constructing optimal video bitrate ladders using Visual Information Fidelity features to predict visual quality, aiming to reduce computation time compared to traditional per-title encoding.

Contribution

It proposes using VIF features from uncompressed videos to efficiently predict visual quality for bitrate ladder construction, improving over fixed and exhaustive encoding methods.

Findings

VIF features effectively predict VMAF scores for compressed videos.

The proposed method reduces the need for exhaustive encoding.

Results outperform fixed bitrate ladders in quality and efficiency.

Abstract

Recently proposed perceptually optimized per-title video encoding methods provide better BD-rate savings than fixed bitrate-ladder approaches that have been employed in the past. However, a disadvantage of per-title encoding is that it requires significant time and energy to compute bitrate ladders. Over the past few years, a variety of methods have been proposed to construct optimal bitrate ladders including using low-level features to predict cross-over bitrates, optimal resolutions for each bitrate, predicting visual quality, etc. Here, we deploy features drawn from Visual Information Fidelity (VIF) (VIF features) extracted from uncompressed videos to predict the visual quality (VMAF) of compressed videos. We present multiple VIF feature sets extracted from different scales and subbands of a video to tackle the problem of bitrate ladder construction. Comparisons are made against a fixed bitrate ladder and a bitrate ladder obtained from exhaustive encoding using Bjontegaard delta metrics.