A Color Intensity Invariant Low Level Feature Optimization Framework for Image Quality Assessment
This work addresses image quality assessment for applications in computer vision, but it appears incremental as it builds upon existing methods with specific optimizations.
The paper tackles the problem of image quality assessment by proposing a low-level feature-based technique that incorporates color intensity adaptation and frequency scaling optimization, achieving feasibility in experimental results.
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.