No-Reference Image Quality Assessment with Global-Local Progressive Integration and Semantic-Aligned Quality Transfer

• URL: http://arxiv.org/abs/2408.03885v2
• Date: Mon, 24 Feb 2025 09:19:26 GMT
• Title: No-Reference Image Quality Assessment with Global-Local Progressive Integration and Semantic-Aligned Quality Transfer
• Authors: Xiaoqi Wang, Yun Zhang,
• Abstract summary: We develop a dual-measurement framework that combines vision Transformer (ViT)-based global feature extractor and convolutional neural networks (CNNs)-based local feature extractor.<n>We introduce a semantic-aligned quality transfer method that extends the training data by automatically labeling the quality scores of diverse image content with subjective opinion scores.
• Score: 6.095342999639137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate measurement of image quality without reference signals remains a fundamental challenge in low-level visual perception applications. In this paper, we propose a global-local progressive integration model that addresses this challenge through three key contributions: 1) We develop a dual-measurement framework that combines vision Transformer (ViT)-based global feature extractor and convolutional neural networks (CNNs)-based local feature extractor to comprehensively capture and quantify image distortion characteristics at different granularities. 2) We propose a progressive feature integration scheme that utilizes multi-scale kernel configurations to align global and local features, and progressively aggregates them via an interactive stack of channel-wise self-attention and spatial interaction modules for multi-grained quality-aware representations. 3) We introduce a semantic-aligned quality transfer method that extends the training data by automatically labeling the quality scores of diverse image content with subjective opinion scores. Experimental results demonstrate that our model yields 5.04% and 5.40% improvements in Spearman's rank-order correlation coefficient (SROCC) for cross-authentic and cross-synthetic dataset generalization tests, respectively. Furthermore, the proposed semantic-aligned quality transfer further yields 2.26% and 13.23% performance gains in evaluations on single-synthetic and cross-synthetic datasets.

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