DCT Approximations Based on Chen's Factorization

TL;DR

This paper introduces two low-complexity, multiplication-free 8-point DCT approximations based on Chen's factorization, demonstrating their effectiveness in image and video compression with improved performance over traditional methods.

Contribution

The paper proposes novel 8-point DCT approximations using Chen's factorization and extends them to 16- and 32-point versions, integrating into HEVC for efficient video coding.

Findings

Proposed transforms outperform traditional DCT in compression tasks.

Low computational complexity makes them suitable for real-time applications.

Embedded into HEVC, they achieve competitive coding performance.

Abstract

In this paper, two 8-point multiplication-free DCT approximations based on the Chen's factorization are proposed and their fast algorithms are also derived. Both transformations are assessed in terms of computational cost, error energy, and coding gain. Experiments with a JPEG-like image compression scheme are performed and results are compared with competing methods. The proposed low-complexity transforms are scaled according to Jridi-Alfalou-Meher algorithm to effect 16- and 32-point approximations. The new sets of transformations are embedded into an HEVC reference software to provide a fully HEVC-compliant video coding scheme. We show that approximate transforms can outperform traditional transforms and state-of-the-art methods at a very low complexity cost.