Image Quality Assessment: Exploring Regional Heterogeneity via Response of Adaptive Multiple Quality Factors in Dictionary Space
Journal:
arXiv
Published Date:
Dec 24, 2024
Abstract
Given that the factors influencing image quality vary significantly with
scene, content, and distortion type, particularly in the context of regional
heterogeneity, we propose an adaptive multi-quality factor (AMqF) framework to
represent image quality in a dictionary space, enabling the precise capture of
quality features in non-uniformly distorted regions. By designing an adapter,
the framework can flexibly decompose quality factors (such as brightness,
structure, contrast, etc.) that best align with human visual perception and
quantify them into discrete visual words. These visual words respond to the
constructed dictionary basis vector, and by obtaining the corresponding
coordinate vectors, we can measure visual similarity. Our method offers two key
contributions. First, an adaptive mechanism that extracts and decomposes
quality factors according to human visual perception principles enhances their
representation ability through reconstruction constraints. Second, the
construction of a comprehensive and discriminative dictionary space and basis
vector allows quality factors to respond effectively to the dictionary basis
vector and capture non-uniform distortion patterns in images, significantly
improving the accuracy of visual similarity measurement. The experimental
results demonstrate that the proposed method outperforms existing
state-of-the-art approaches in handling various types of distorted images. The
source code is available at https://anonymous.4open.science/r/AMqF-44B2.
