Energy-efficient 8-point DCT Approximations: Theory and Hardware Architectures

TL;DR

This paper introduces new low-complexity 8-point DCT approximations that significantly reduce power consumption and computational costs, suitable for energy-efficient image compression hardware implementations.

Contribution

The paper proposes novel 8-point DCT approximations based on pruning existing methods, with hardware implementations demonstrating substantial energy savings.

Findings

Achieved 21-25% power reduction in hardware implementations.

Proposed transforms exhibit very low arithmetic complexity.

Maintained good image compression performance.

Abstract

Due to its remarkable energy compaction properties, the discrete cosine transform (DCT) is employed in a multitude of compression standards, such as JPEG and H.265/HEVC. Several low-complexity integer approximations for the DCT have been proposed for both 1-D and 2-D signal analysis. The increasing demand for low-complexity, energy efficient methods require algorithms with even lower computational costs. In this paper, new 8-point DCT approximations with very low arithmetic complexity are presented. The new transforms are proposed based on pruning state-of-the-art DCT approximations. The proposed algorithms were assessed in terms of arithmetic complexity, energy retention capability, and image compression performance. In addition, a metric combining performance and computational complexity measures was proposed. Results showed good performance and extremely low computational complexity. Introduced algorithms were mapped into systolic-array digital architectures and physically realized as digital prototype circuits using FPGA technology and mapped to 45nm CMOS technology. All hardware-related metrics showed low resource consumption of the proposed pruned approximate transforms. The best proposed transform according to the introduced metric presents a reduction in power consumption of 21--25%.