Beyond MOS: Subjective Image Quality Score Preprocessing Method Based on Perceptual Similarity

• URL: http://arxiv.org/abs/2404.19666v1
• Date: Tue, 30 Apr 2024 16:01:14 GMT
• Title: Beyond MOS: Subjective Image Quality Score Preprocessing Method Based on Perceptual Similarity
• Authors: Lei Wang, Desen Yuan,
• Abstract summary: ITU-R BT.500, ITU-T P.910, and ITU-T P.913 have been standardized to clean up the original opinion scores. PSP exploit the perceptual similarity between images to alleviate subjective bias in less annotated scenarios.
• Score: 2.290956583394892
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image quality assessment often relies on raw opinion scores provided by subjects in subjective experiments, which can be noisy and unreliable. To address this issue, postprocessing procedures such as ITU-R BT.500, ITU-T P.910, and ITU-T P.913 have been standardized to clean up the original opinion scores. These methods use annotator-based statistical priors, but they do not take into account extensive information about the image itself, which limits their performance in less annotated scenarios. Generally speaking, image quality datasets usually contain similar scenes or distortions, and it is inevitable for subjects to compare images to score a reasonable score when scoring. Therefore, In this paper, we proposed Subjective Image Quality Score Preprocessing Method perceptual similarity Subjective Preprocessing (PSP), which exploit the perceptual similarity between images to alleviate subjective bias in less annotated scenarios. Specifically, we model subjective scoring as a conditional probability model based on perceptual similarity with previously scored images, called subconscious reference scoring. The reference images are stored by a neighbor dictionary, which is obtained by a normalized vector dot-product based nearest neighbor search of the images' perceptual depth features. Then the preprocessed score is updated by the exponential moving average (EMA) of the subconscious reference scoring, called similarity regularized EMA. Our experiments on multiple datasets (LIVE, TID2013, CID2013) show that this method can effectively remove the bias of the subjective scores. Additionally, Experiments prove that the Preprocesed dataset can improve the performance of downstream IQA tasks very well.

Related papers

• DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild [54.139923409101044]
  We propose a novel IQA method called diffusion priors-based IQA (DP-IQA) We use pre-trained stable diffusion as the backbone, extract multi-level features from the denoising U-Net, and decode them to estimate the image quality score. We distill the knowledge in the above model into a CNN-based student model, significantly reducing the parameter to enhance applicability.
  arXiv  Detail & Related papers  (2024-05-30T12:32:35Z)
• Regression-free Blind Image Quality Assessment with Content-Distortion Consistency [42.683300312253884]
  We propose a regression-free framework for image quality evaluation. It is based upon retrieving locally similar instances by incorporating semantic and distortion feature spaces. The proposed method achieves competitive, even superior performance compared to state-of-the-art regression-based methods.
  arXiv  Detail & Related papers  (2023-07-18T14:19:28Z)
• Balancing the Picture: Debiasing Vision-Language Datasets with Synthetic Contrast Sets [52.77024349608834]
  Vision-language models can perpetuate and amplify societal biases learned during pre-training on uncurated image-text pairs from the internet. COCO Captions is the most commonly used dataset for evaluating bias between background context and the gender of people in-situ. We propose a novel dataset debiasing pipeline to augment the COCO dataset with synthetic, gender-balanced contrast sets.
  arXiv  Detail & Related papers  (2023-05-24T17:59:18Z)
• PIQI: Perceptual Image Quality Index based on Ensemble of Gaussian Process Regression [2.9412539021452715]
  Perceptual Image Quality Index (PIQI) is proposed to assess the quality of digital images. The performance of the PIQI is checked on six benchmark databases and compared with twelve state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-16T06:44:17Z)
• Masked Images Are Counterfactual Samples for Robust Fine-tuning [77.82348472169335]
  Fine-tuning deep learning models can lead to a trade-off between in-distribution (ID) performance and out-of-distribution (OOD) robustness. We propose a novel fine-tuning method, which uses masked images as counterfactual samples that help improve the robustness of the fine-tuning model.
  arXiv  Detail & Related papers  (2023-03-06T11:51:28Z)
• Treatment Learning Causal Transformer for Noisy Image Classification [62.639851972495094]
  In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy. Motivated from causal variational inference, we propose a transformer-based architecture, that uses a latent generative model to estimate robust feature representations for noise image classification. We also create new noisy image datasets incorporating a wide range of noise factors for performance benchmarking.
  arXiv  Detail & Related papers  (2022-03-29T13:07:53Z)
• Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment [157.1292674649519]
  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.
  arXiv  Detail & Related papers  (2021-08-18T02:35:08Z)
• Scene Uncertainty and the Wellington Posterior of Deterministic Image Classifiers [68.9065881270224]
  We introduce the Wellington Posterior, which is the distribution of outcomes that would have been obtained in response to data that could have been generated by the same scene. We explore the use of data augmentation, dropout, ensembling, single-view reconstruction, and model linearization to compute a Wellington Posterior. Additional methods include the use of conditional generative models such as generative adversarial networks, neural radiance fields, and conditional prior networks.
  arXiv  Detail & Related papers  (2021-06-25T20:10:00Z)
• A combined full-reference image quality assessment approach based on convolutional activation maps [0.0]
  The goal of full-reference image quality assessment (FR-IQA) is to predict the quality of an image as perceived by human observers with using its pristine, reference counterpart. In this study, we explore a novel, combined approach which predicts the perceptual quality of a distorted image by compiling a feature vector from convolutional activation maps.
  arXiv  Detail & Related papers  (2020-10-19T10:00:29Z)
• Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals [27.539001365348906]
  We present a method for generating counterfactuals by incorporating a structural causal model (SCM) in an improved variant of Adversarially Learned Inference (ALI) We show how to explain a pre-trained machine learning classifier, evaluate its bias, and mitigate the bias using a counterfactual regularizer.
  arXiv  Detail & Related papers  (2020-09-17T13:19:31Z)

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.