A two-stage video coding framework with both self-adaptive redundant dictionary and adaptively orthonormalized DCT basis

TL;DR

This paper introduces a two-stage video coding framework that uses a self-adaptive dictionary and an adaptively orthonormalized DCT basis, significantly improving rate-distortion performance over previous methods and competing with standard codecs.

Contribution

The paper presents a novel two-stage coding framework with a self-adaptive dictionary and orthonormalized DCT basis, enhancing video coding efficiency beyond prior single-stage approaches.

Findings

Outperforms previous one-stage coder in RD performance.

Surpasses H.264 (x264) and is competitive with HEVC (HM).

Uses a context adaptive entropy coder for efficient coding.

Abstract

In this work, we propose a two-stage video coding framework, as an extension of our previous one-stage framework in [1]. The two-stage frameworks consists two different dictionaries. Specifically, the first stage directly finds the sparse representation of a block with a self-adaptive dictionary consisting of all possible inter-prediction candidates by solving an L0-norm minimization problem using an improved orthogonal matching pursuit with embedded orthonormalization (eOMP) algorithm, and the second stage codes the residual using DCT dictionary adaptively orthonormalized to the subspace spanned by the first stage atoms. The transition of the first stage and the second stage is determined based on both stages' quantization stepsizes and a threshold. We further propose a complete context adaptive entropy coder to efficiently code the locations and the coefficients of chosen first stage atoms. Simulation results show that the proposed coder significantly improves the RD performance over our previous one-stage coder. More importantly, the two-stage coder, using a fixed block size and inter-prediction only, outperforms the H.264 coder (x264) and is competitive with the HEVC reference coder (HM) over a large rate range.