Feature Denoising Diffusion Model for Blind Image Quality Assessment

• URL: http://arxiv.org/abs/2401.11949v1
• Date: Mon, 22 Jan 2024 13:38:24 GMT
• Title: Feature Denoising Diffusion Model for Blind Image Quality Assessment
• Authors: Xudong Li, Jingyuan Zheng, Runze Hu, Yan Zhang, Ke Li, Yunhang Shen, Xiawu Zheng, Yutao Liu, ShengChuan Zhang, Pingyang Dai, Rongrong Ji
• Abstract summary: Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Deep learning BIQA methods typically depend on using features from high-level tasks for transfer learning. In this paper, we take an initial step towards exploring the diffusion model for feature denoising in BIQA.
• Score: 58.5808754919597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Currently, deep learning BIQA methods typically depend on using features from high-level tasks for transfer learning. However, the inherent differences between BIQA and these high-level tasks inevitably introduce noise into the quality-aware features. In this paper, we take an initial step towards exploring the diffusion model for feature denoising in BIQA, namely Perceptual Feature Diffusion for IQA (PFD-IQA), which aims to remove noise from quality-aware features. Specifically, (i) We propose a {Perceptual Prior Discovery and Aggregation module to establish two auxiliary tasks to discover potential low-level features in images that are used to aggregate perceptual text conditions for the diffusion model. (ii) We propose a Perceptual Prior-based Feature Refinement strategy, which matches noisy features to predefined denoising trajectories and then performs exact feature denoising based on text conditions. Extensive experiments on eight standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods, i.e., achieving the PLCC values of 0.935 ( vs. 0.905 in KADID) and 0.922 ( vs. 0.894 in LIVEC).

Related papers

• Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment [15.235786583920062]
  Blind image quality assessment (BIQA) serves as a fundamental task in computer vision, yet it often fails to consistently align with human subjective perception. Recent advances show that multi-scale evaluation strategies are promising due to their ability to replicate the hierarchical structure of human vision. This paper addresses two primary challenges: the significant redundancy of information across different scales, and the confusion caused by combining features from these scales.
  arXiv  Detail & Related papers  (2024-11-13T20:17:30Z)
• Boosting CLIP Adaptation for Image Quality Assessment via Meta-Prompt Learning and Gradient Regularization [55.09893295671917]
  This paper introduces a novel Gradient-Regulated Meta-Prompt IQA Framework (GRMP-IQA) The GRMP-IQA comprises two key modules: Meta-Prompt Pre-training Module and Quality-Aware Gradient Regularization. Experiments on five standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods under limited data setting.
  arXiv  Detail & Related papers  (2024-09-09T07:26:21Z)
• DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild [54.139923409101044]
  Blind image quality assessment (IQA) in the wild presents significant challenges. Given the difficulty in collecting large-scale training data, leveraging limited data to develop a model with strong generalization remains an open problem. Motivated by the robust image perception capabilities of pre-trained text-to-image (T2I) diffusion models, we propose a novel IQA method, diffusion priors-based IQA.
  arXiv  Detail & Related papers  (2024-05-30T12:32:35Z)
• Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
  Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings. This paper introduces an innovative multi-modal prompt-based methodology for IQA.
  arXiv  Detail & Related papers  (2024-04-23T11:45:32Z)
• Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity [55.399230250413986]
  We propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) to remove harmful semantic noise features from the upstream task. Our approach achieves superior performance to the state-of-the-art NR-IQA methods on eight standard IQA datasets.
  arXiv  Detail & Related papers  (2023-12-11T06:50:27Z)
• Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment [58.09173822651016]
  We argue that it is possible to learn reference knowledge under the No-Reference Image Quality Assessment setting. We propose a new framework to learn comparative knowledge from non-aligned reference images. Experiments on eight standard NR-IQA datasets demonstrate the superior performance to the state-of-the-art NR-IQA methods.
  arXiv  Detail & Related papers  (2023-12-01T13:56:01Z)
• Data-Efficient Image Quality Assessment with Attention-Panel Decoder [19.987556370430806]
  Blind Image Quality Assessment (BIQA) is a fundamental task in computer vision, which remains unresolved due to the complex distortion conditions and diversified image contents. We propose a novel BIQA pipeline based on the Transformer architecture, which achieves an efficient quality-aware feature representation with much fewer data.
  arXiv  Detail & Related papers  (2023-04-11T03:52:17Z)
• No-Reference Image Quality Assessment via Feature Fusion and Multi-Task Learning [29.19484863898778]
  Blind or no-reference image quality assessment (NR-IQA) is a fundamental, unsolved, and yet challenging problem. We propose a simple and yet effective general-purpose no-reference (NR) image quality assessment framework based on multi-task learning. Our model employs distortion types as well as subjective human scores to predict image quality.
  arXiv  Detail & Related papers  (2020-06-06T05:04:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.