Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment

• URL: http://arxiv.org/abs/2108.07948v1
• Date: Wed, 18 Aug 2021 02:35:08 GMT
• Title: Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment
• Authors: Heliang Zheng, Huan Yang, Jianlong Fu, Zheng-Jun Zha, Jiebo Luo
• Abstract summary: We propose a practical solution named degraded-reference IQA (DR-IQA) DR-IQA exploits the inputs of IR models, degraded images, as references. Our results can even be close to the performance of full-reference settings.
• Score: 157.1292674649519
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: An important scenario for image quality assessment (IQA) is to evaluate image restoration (IR) algorithms. The state-of-the-art approaches adopt a full-reference paradigm that compares restored images with their corresponding pristine-quality images. However, pristine-quality images are usually unavailable in blind image restoration tasks and real-world scenarios. In this paper, we propose a practical solution named degraded-reference IQA (DR-IQA), which exploits the inputs of IR models, degraded images, as references. Specifically, we extract reference information from degraded images by distilling knowledge from pristine-quality images. The distillation is achieved through learning a reference space, where various degraded images are encouraged to share the same feature statistics with pristine-quality images. And the reference space is optimized to capture deep image priors that are useful for quality assessment. Note that pristine-quality images are only used during training. Our work provides a powerful and differentiable metric for blind IRs, especially for GAN-based methods. Extensive experiments show that our results can even be close to the performance of full-reference settings.

Related papers

• Dual-Representation Interaction Driven Image Quality Assessment with Restoration Assistance [11.983231834400698]
  No-Reference Image Quality Assessment for distorted images has always been a challenging problem due to image content variance and distortion diversity. Previous IQA models mostly encode explicit single-quality features of synthetic images to obtain quality-aware representations for quality score prediction. We introduce the DRI method to obtain degradation vectors and quality vectors of images, which separately model the degradation and quality information of low-quality images.
  arXiv  Detail & Related papers  (2024-11-26T12:48:47Z)
• Fine-grained subjective visual quality assessment for high-fidelity compressed images [4.787528476079247]
  The JPEG standardization project AIC is developing a subjective image quality assessment methodology for high-fidelity images. This paper presents the proposed assessment methods, a dataset of high-quality compressed images, and their corresponding crowdsourced visual quality ratings. It also outlines a data analysis approach that reconstructs quality scale values in just noticeable difference (JND) units.
  arXiv  Detail & Related papers  (2024-10-12T11:37:19Z)
• 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)
• Image Aesthetics Assessment via Learnable Queries [59.313054821874864]
  We propose the Image Aesthetics Assessment via Learnable Queries (IAA-LQ) approach. It adapts learnable queries to extract aesthetic features from pre-trained image features obtained from a frozen image encoder. Experiments on real-world data demonstrate the advantages of IAA-LQ, beating the best state-of-the-art method by 2.2% and 2.1% in terms of SRCC and PLCC, respectively.
  arXiv  Detail & Related papers  (2023-09-06T09:42:16Z)
• Conformer and Blind Noisy Students for Improved Image Quality Assessment [80.57006406834466]
  Learning-based approaches for perceptual image quality assessment (IQA) usually require both the distorted and reference image for measuring the perceptual quality accurately. In this work, we explore the performance of transformer-based full-reference IQA models. We also propose a method for IQA based on semi-supervised knowledge distillation from full-reference teacher models into blind student models.
  arXiv  Detail & Related papers  (2022-04-27T10:21:08Z)
• Confusing Image Quality Assessment: Towards Better Augmented Reality Experience [96.29124666702566]
  We consider AR technology as the superimposition of virtual scenes and real scenes, and introduce visual confusion as its basic theory. A ConFusing Image Quality Assessment (CFIQA) database is established, which includes 600 reference images and 300 distorted images generated by mixing reference images in pairs. An objective metric termed CFIQA is also proposed to better evaluate the confusing image quality.
  arXiv  Detail & Related papers  (2022-04-11T07:03:06Z)
• Can No-reference features help in Full-reference image quality estimation? [20.491565297561912]
  We study utilization of no-reference features in Full-reference IQA task. Our model achieves higher SRCC and KRCC scores than a number of state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2022-03-02T03:39:28Z)
• Image Quality Assessment using Contrastive Learning [50.265638572116984]
  We train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem. We show through extensive experiments that CONTRIQUE achieves competitive performance when compared to state-of-the-art NR image quality models. Our results suggest that powerful quality representations with perceptual relevance can be obtained without requiring large labeled subjective image quality datasets.
  arXiv  Detail & Related papers  (2021-10-25T21:01:00Z)
• A survey on IQA [0.0]
  This article will review the concepts and metrics of image quality assessment and also video quality assessment. It briefly introduce some methods of full-reference and semi-reference image quality assessment, and focus on the non-reference image quality assessment methods based on deep learning.
  arXiv  Detail & Related papers  (2021-08-29T10:52:27Z)
• Perceptual Image Restoration with High-Quality Priori and Degradation Learning [28.93489249639681]
  We show that our model performs well in measuring the similarity between restored and degraded images. Our simultaneous restoration and enhancement framework generalizes well to real-world complicated degradation types.
  arXiv  Detail & Related papers  (2021-03-04T13:19:50Z)

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.