Exploring the Distributed Video Coding in a Quality Assessment Context

TL;DR

This paper introduces a novel method for generating higher quality side information in distributed video coding by optimizing motion compensation with human visual system-based quality metrics, leading to improved compression efficiency.

Contribution

It proposes a new approach to generate side information using perceptual quality metrics, enhancing the effectiveness of distributed video coding compared to traditional methods.

Findings

Enhanced side information quality improves compression performance.

Perceptual metric-based motion compensation outperforms MSE/SAD-based methods.

Better compression results in lower bitrates for similar visual quality.

Abstract

In the popular video coding trend, the encoder has the task to exploit both spatial and temporal redundancies present in the video sequence, which is a complex procedure. As a result almost all video encoders have five to ten times more complexity than their decoders. In a video compression process, one of the main tasks at the encoder side is motion estimation which is to extract the temporal correlation between frames. Distributed video coding (DVC) proposed the idea that can lead to low complexity encoders and higher complexity decoders. DVC is a new paradigm in video compression based on the information theoretic ideas of Slepian-Wolf and Wyner-Ziv theorems. Wyner-Ziv coding is naturally robust against transmission errors and can be used for joint source and channel coding. Side Information is one of the key components of the Wyner-Ziv decoder. Better side information generation will result in better functionality of Wyner-Ziv coder. In this paper we proposed a new method that can generate side information with a better quality and thus better compression. We have used HVS (human visual system) based image quality metrics as our quality criterion. The motion estimation we used in the decoder is modified due to these metrics such that we could obtain finer side information. The motion compensation is optimized for perceptual quality metrics and leads to better side information generation compared to con- ventional MSE (mean squared error) or SAD (sum of absolute difference) based motion compensation currently used in the literature. Better motion compensation means better compression.