No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation

• URL: http://arxiv.org/abs/2008.08561v4
• Date: Wed, 26 May 2021 03:00:56 GMT
• Title: No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation
• Authors: Baoliang Chen, Haoliang Li, Hongfei Fan and Shiqi Wang
• Abstract summary: We develop the first unsupervised domain adaptation based no reference quality assessment method for SCIs. Inspired by the transferability of pair-wise relationship, the proposed quality measure operates based on the philosophy of improving the transferability and discriminability simultaneously. Our method can achieve higher performance on different source-target settings based on a light-weight convolution neural network.
• Score: 37.1611601418026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we quest the capability of transferring the quality of natural scene images to the images that are not acquired by optical cameras (e.g., screen content images, SCIs), rooted in the widely accepted view that the human visual system has adapted and evolved through the perception of natural environment. Here, we develop the first unsupervised domain adaptation based no reference quality assessment method for SCIs, leveraging rich subjective ratings of the natural images (NIs). In general, it is a non-trivial task to directly transfer the quality prediction model from NIs to a new type of content (i.e., SCIs) that holds dramatically different statistical characteristics. Inspired by the transferability of pair-wise relationship, the proposed quality measure operates based on the philosophy of improving the transferability and discriminability simultaneously. In particular, we introduce three types of losses which complementarily and explicitly regularize the feature space of ranking in a progressive manner. Regarding feature discriminatory capability enhancement, we propose a center based loss to rectify the classifier and improve its prediction capability not only for source domain (NI) but also the target domain (SCI). For feature discrepancy minimization, the maximum mean discrepancy (MMD) is imposed on the extracted ranking features of NIs and SCIs. Furthermore, to further enhance the feature diversity, we introduce the correlation penalization between different feature dimensions, leading to the features with lower rank and higher diversity. Experiments show that our method can achieve higher performance on different source-target settings based on a light-weight convolution neural network. The proposed method also sheds light on learning quality assessment measures for unseen application-specific content without the cumbersome and costing subjective evaluations.

Related papers

• Bridging the Synthetic-to-Authentic Gap: Distortion-Guided Unsupervised Domain Adaptation for Blind Image Quality Assessment [46.64517740627379]
  Training on synthetic data is expected to be beneficial, but synthetically trained models often suffer from poor generalization in real domains due to domain gaps. In this work, we make a key observation that introducing more distortion types in the synthetic dataset may not improve or even be harmful to generalizing authentic image quality assessment. We propose distortion-guided unsupervised domain adaptation for BIQA (DGQA), a novel framework that leverages adaptive multi-domain selection via prior knowledge from distortion.
  arXiv  Detail & Related papers  (2024-05-07T10:07:33Z)
• 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)
• DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator [53.57431705309919]
  ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models. We develop a novel full-reference IQA (FR-IQA) model based exclusively on pre-trained DNN features. We conduct comprehensive experiments to demonstrate the superiority of the proposed quality model on five standard IQA datasets.
  arXiv  Detail & Related papers  (2022-11-09T14:57:27Z)
• StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment [23.289183622856704]
  We propose an effective perception-oriented unsupervised domain adaptation method StyleAM for NR-IQA. StyleAM transfers sufficient knowledge from label-rich source domain data to label-free target domain images via Style Alignment and Mixup. Experiments on two typical cross-domain settings have demonstrated the effectiveness of our proposed StyleAM on NR-IQA.
  arXiv  Detail & Related papers  (2022-07-29T05:51:18Z)
• Textural-Structural Joint Learning for No-Reference Super-Resolution Image Quality Assessment [59.91741119995321]
  We develop a dual stream network to jointly explore the textural and structural information for quality prediction, dubbed TSNet. By mimicking the human vision system (HVS) that pays more attention to the significant areas of the image, we develop the spatial attention mechanism to make the visual-sensitive areas more distinguishable. Experimental results show the proposed TSNet predicts the visual quality more accurate than the state-of-the-art IQA methods, and demonstrates better consistency with the human's perspective.
  arXiv  Detail & Related papers  (2022-05-27T09:20:06Z)
• No-Reference Point Cloud Quality Assessment via Domain Adaptation [31.280188860021248]
  We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA) for 3D point clouds. In particular, we treat natural images as the source domain and point clouds as the target domain, and infer point cloud quality via unsupervised adversarial domain adaptation. Experimental results show that the proposed method can achieve higher performance than traditional no-reference metrics, even comparable results with full-reference metrics.
  arXiv  Detail & Related papers  (2021-12-06T08:20:40Z)
• No-Reference Image Quality Assessment by Hallucinating Pristine Features [24.35220427707458]
  We propose a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination. The effectiveness of our proposed method is demonstrated on four popular IQA databases.
  arXiv  Detail & Related papers  (2021-08-09T16:48:34Z)
• A-FMI: Learning Attributions from Deep Networks via Feature Map Importance [58.708607977437794]
  Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs) The redundancy of attribution features and the gradient saturation problem are challenges that attribution methods still face. We propose a new concept, feature map importance (FMI), to refine the contribution of each feature map, and a novel attribution method via FMI, to address the gradient saturation problem.
  arXiv  Detail & Related papers  (2021-04-12T14:54:44Z)
• Style Normalization and Restitution for DomainGeneralization and Adaptation [88.86865069583149]
  An effective domain generalizable model is expected to learn feature representations that are both generalizable and discriminative. In this paper, we design a novel Style Normalization and Restitution module (SNR) to ensure both high generalization and discrimination capability of the networks.
  arXiv  Detail & Related papers  (2021-01-03T09:01:39Z)

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.