Multiple Selection Approximation for Improved Spatio-Temporal Prediction in Video Coding

TL;DR

This paper introduces a new spatio-temporal prediction algorithm for video coding that combines motion compensation with spatial refinement, resulting in better compression efficiency and higher quality video reconstruction.

Contribution

The paper presents a novel two-stage prediction algorithm that outperforms previous methods in speed and accuracy by integrating temporal and spatial redundancies.

Findings

Up to 15% reduction in data rate.

Up to 1.16 dB PSNR gain.

Faster prediction process than previous algorithms.

Abstract

In this contribution, a novel spatio-temporal prediction algorithm for video coding is introduced. This algorithm exploits temporal as well as spatial redundancies for effectively predicting the signal to be encoded. To achieve this, the algorithm operates in two stages. Initially, motion compensated prediction is applied on the block being encoded. Afterwards this preliminary temporal prediction is refined by forming a joint model of the initial predictor and the spatially adjacent already transmitted blocks. The novel algorithm is able to outperform earlier refinement algorithms in speed and prediction quality. Compared to pure motion compensated prediction, the mean data rate can be reduced by up to 15% and up to 1.16 dB gain in PSNR can be achieved for the considered sequences.