Acceleration of Histogram-Based Contrast Enhancement via Selective Downsampling

TL;DR

This paper introduces a framework that accelerates histogram-based image contrast enhancement algorithms by using selective downsampling and a novel mapping function, significantly reducing computation time while maintaining image quality.

Contribution

The paper presents a new acceleration framework for histogram-based contrast enhancement that employs spatial and gray-level downsampling along with mapping function calibration, applicable to various algorithms.

Findings

HE speed increased by 3.9 times

SMIRANK speed increased by 13.5 times

Image quality preserved despite acceleration

Abstract

In this paper, we propose a general framework to accelerate the universal histogram-based image contrast enhancement (CE) algorithms. Both spatial and gray-level selective down-sampling of digital images are adopted to decrease computational cost, while the visual quality of enhanced images is still preserved and without apparent degradation. Mapping function calibration is novelly proposed to reconstruct the pixel mapping on the gray levels missed by downsampling. As two case studies, accelerations of histogram equalization (HE) and the state-of-the-art global CE algorithm, i.e., spatial mutual information and PageRank (SMIRANK), are presented detailedly. Both quantitative and qualitative assessment results have verified the effectiveness of our proposed CE acceleration framework. In typical tests, computational efficiencies of HE and SMIRANK have been speeded up by about 3.9 and 13.5 times, respectively.