Referenceless Quality Assessment for Contrast Distorted Image Using Hybrid Features

Abstract

Contrast distortion has a significant influence on the perceptual quality of an image, which may be generated in various image processing procedures. In this paper, we propose a no-reference image quality assessment (IQA) algorithm for contrast-distorted images based on hybrid features from information and appearance attributes. From information attribute aspect, we utilize the basic information feature to quantify the information of the visible part and the extended information feature further containing the invisible part of an image. From the appearance aspect, we propose an efficient perceptual contrast and colorfulness index to capture the direct visual changes. With the hybrid information attribute and appearance attribute features, support vector regression (SVR) is utilized to learn an IQA model to predict the quality of contrast distorted images. Extensive experimental results on CCID2014 and TID2013 databases further demonstrate the superior performance and robustness of the proposed method.