Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy

TLDR

Histogram‑based image processing is well established, yet its application to transform‑domain coefficient histograms remains largely unexplored. The study introduces three logarithmic‑transform histogram‑based enhancement techniques: matching, shifting, and shaping with Gaussian distributions. These methods exploit logarithmic transform domain histogram properties and equalization, incorporating a human‑visual‑system contrast metric to select optimal parameters and transforms efficiently. Experiments show that the proposed algorithms improve image contrast and visual quality compared to baseline approaches.

Abstract

Many applications of histograms for the purposes of image processing are well known. However, applying this process to the transform domain by way of a transform coefficient histogram has not yet been fully explored. This paper proposes three methods of image enhancement: a) logarithmic transform histogram matching, b) logarithmic transform histogram shifting, and c) logarithmic transform histogram shaping using Gaussian distributions. They are based on the properties of the logarithmic transform domain histogram and histogram equalization. The presented algorithms use the fact that the relationship between stimulus and perception is logarithmic and afford a marriage between enhancement qualities and computational efficiency. A human visual system-based quantitative measurement of image contrast improvement is also defined. This helps choose the best parameters and transform for each enhancement. A number of experimental results are presented to illustrate the performance of the proposed algorithms.