A Color Intensity Invariant Low Level Feature Optimization Framework for Image Quality Assessment

TL;DR

This paper introduces a novel low-level feature-based image quality assessment method that optimizes color intensity and frequency scaling, improving perceptual quality evaluation by integrating object detection concepts.

Contribution

It presents a new IQA framework combining filter-bank decomposition, color intensity adaptation, and frequency scaling optimization within a unified architecture.

Findings

Demonstrates improved correlation with human perceptual scores

Validates the approach on unbiased IQA datasets

Shows robustness across different image distortions

Abstract

Image Quality Assessment (IQA) algorithms evaluate the perceptual quality of an image using evaluation scores that assess the similarity or difference between two images. We propose a new low-level feature based IQA technique, which applies filter-bank decomposition and center-surround methodology. Differing from existing methods, our model incorporates color intensity adaptation and frequency scaling optimization at each filter-bank level and spatial orientation to extract and enhance perceptually significant features. Our computational model exploits the concept of object detection and encapsulates characteristics proposed in other IQA algorithms in a unified architecture. We also propose a systematic approach to review the evolution of IQA algorithms using unbiased test datasets, instead of looking at individual scores in isolation. Experimental results demonstrate the feasibility of our approach.