A Perceptual Based Motion Compensation Technique for Video Coding

TL;DR

This paper introduces a novel motion estimation method for video coding that uses perceptual image quality metrics like SSIM, CW-SSIM, and VIF instead of traditional error metrics, leading to improved compression efficiency.

Contribution

It proposes incorporating perceptual image quality metrics into motion estimation, enhancing video compression without sacrificing perceived quality.

Findings

Improved compression rate at fixed quality levels.

Better visual quality in coded videos at the same bit budget.

Use of perceptual metrics enhances motion estimation effectiveness.

Abstract

Motion estimation is one of the important procedures in the all video encoders. Most of the complexity of the video coder depends on the complexity of the motion estimation step. The original motion estimation algorithm has a remarkable complexity and therefore many improvements were proposed to enhance the crude version of the motion estimation. The basic idea of many of these works were to optimize some distortion function for mean squared error (MSE) or sum of absolute difference (SAD) in block matching But it is shown that these metrics do not conclude the quality as it is, on the other hand, they are not compatible with the human visual system (HVS). In this paper we explored the usage of the image quality metrics in the video coding and more specific in the motion estimation. We have utilized the perceptual image quality metrics instead of MSE or SAD in the block based motion estimation. Three different metrics have used: structural similarity or SSIM, complex wavelet structural similarity or CW-SSIM, visual information fidelity or VIF. Experimental results showed that usage of the quality criterions can improve the compression rate while the quality remains fix and thus better quality in coded video at the same bit budget.