Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Abstract

Image quality assessment (IQA) has been a topic of intense research over the last several decades. With each year comes an increasing number of new IQA algorithms, extensions of existing IQA algorithms, and applications of IQA to other disciplines. In this article, I first provide an up-to-date review of research in IQA, and then I highlight several open challenges in this field. The first half of this article provides discuss key properties of visual perception, image quality databases, existing full-reference, no-reference, and reduced-reference IQA algorithms. Yet, despite the remarkable progress that has been made in IQA, many fundamental challenges remain largely unsolved. The second half of this article highlights some of these challenges. I specifically discuss challenges related to lack of complete perceptual models for: natural images, compound and suprathreshold distortions, and multiple distortions, and the interactive effects of these distortions on the images. I also discuss challenges related to IQA of images containing nontraditional, and I discuss challenges related to the computational efficiency. The goal of this article is not only to help practitioners and researchers keep abreast of the recent advances in IQA, but to also raise awareness of the key limitations of current IQA knowledge.

A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

“…In order to compare IQA approaches [1], specific benchmark image datasets can be used. They contain pristine, distortion-free images, their corresponding distorted equivalents and human scores.…”

“…Human evaluation of such images can be either inconvenient or expensive. Therefore, in order to measure image quality from the human perception point of view, many image quality assessment (IQA) measures have been developed [1], [2]. They are divided into three categories.…”

Image Quality Assessment Based on Multi-Order Local Features Description, Modeling and Quantification

“…With the widespread use of digital images, images are usually affected by a wide variety of distortions during acquisition, compression, storage, transmission and reproduction, resulting in perceptual quality degradation [1]- [5]. Quantification and assessment for such degradation of image quality play an important role in image processing system.…”

“…Image quality assessment (IQA) can provide feedback for processing system quality monitoring, evaluation and optimization. It has become a hot topic [1]- [7].…”

“…Moreover, subjective assessment is a laborious, expensive, time-consuming and non-repeatable process [8], [9]. Due to the weaknesses of subjective assessment, recent researches are focused on developing objective metrics to predict image quality automatically that are consistent with the subjective evaluation [1]- [3], [6]- [8].…”