Regression-free Blind Image Quality Assessment with Content-Distortion Consistency

• URL: http://arxiv.org/abs/2307.09279v2
• Date: Sat, 21 Oct 2023 07:50:38 GMT
• Title: Regression-free Blind Image Quality Assessment with Content-Distortion Consistency
• Abstract summary: 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.
• Score: 42.683300312253884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The optimization objective of regression-based blind image quality assessment (IQA) models is to minimize the mean prediction error across the training dataset, which can lead to biased parameter estimation due to potential training data biases. To mitigate this issue, we propose a regression-free framework for image quality evaluation, which is based upon retrieving locally similar instances by incorporating semantic and distortion feature spaces. The approach is motivated by the observation that the human visual system (HVS) exhibits analogous perceptual responses to semantically similar image contents impaired by identical distortions, which we term as content-distortion consistency. The proposed method constructs a hierarchical k-nearest neighbor (k-NN) algorithm for instance retrieval through two classification modules: semantic classification (SC) module and distortion classification (DC) module. Given a test image and an IQA database, the SC module retrieves multiple pristine images semantically similar to the test image. The DC module then retrieves instances based on distortion similarity from the distorted images that correspond to each retrieved pristine image. Finally, quality prediction is obtained by aggregating the subjective scores of the retrieved instances. Without training on subjective quality scores, the proposed regression-free method achieves competitive, even superior performance compared to state-of-the-art regression-based methods on authentic and synthetic distortion IQA benchmarks.

Related papers

• Pixel Perfect: Relational Image Quality Assessment with Spatially-Aware Distortions [12.998174707153844]
  Traditional image quality assessment (IQA) methods rely on mean opinion scores (MOS)<n>We overcome these limitations by shifting from absolute quality prediction to a relational and directional assessment.<n>A distortion prediction network is trained with an anti-symmetric objective to produce spatially-aware, disentangled maps.<n>A scoring network is trained via contrastive learning on ordinally ranked image sets to predict a relational quality score.
  arXiv  Detail & Related papers  (2026-05-04T17:39:07Z)
• Beyond Fidelity: Semantic Similarity Assessment in Low-Level Image Processing [53.27421114481586]
  We formalize textitSemantic Similarity as a new evaluation task for low-level image processing.<n>We present a structured formulation of image semantics based on semantic entities and their relations.<n>We propose Triplet-based Semantic Similarity Score (T3S), which models image semantics through foreground entities, background entities, and relations.
  arXiv  Detail & Related papers  (2026-04-28T09:21:05Z)
• Causal Disentanglement for Full-Reference Image Quality Assessment [21.96346718260435]
  We propose a novel FR-IQA paradigm based on causal inference and decoupled representation learning.<n>Our approach formulates degradation estimation as a causal disentanglement process guided by intervention on latent representations.<n>Our method achieves highly competitive performance on standard IQA benchmarks across fully supervised, few-label, and label-free settings.
  arXiv  Detail & Related papers  (2026-04-23T13:18:13Z)
• Q-DeepSight: Incentivizing Thinking with Images for Image Quality Assessment and Refinement [58.15004031934379]
  We propose Q-DeepSight, a think-with-image framework that emulates this human-like process.<n>Q-DeepSight achieves state-of-the-art performance across diverse benchmarks, including natural, restored, and AI-generated content.<n>We demonstrate its practical value with Perceptual-in-Generation (PiG), a training-free framework where Q-DeepSight's diagnoses guide iterative image enhancement.
  arXiv  Detail & Related papers  (2026-04-18T06:10:57Z)
• Attention Down-Sampling Transformer, Relative Ranking and Self-Consistency for Blind Image Quality Assessment [17.04649536069553]
  No-reference image quality assessment is a challenging domain that addresses estimating image quality without the original reference. We introduce an improved mechanism to extract local and non-local information from images via different transformer encoders and CNNs. A self-consistency approach to self-supervision is presented, explicitly addressing the degradation of no-reference image quality assessment (NR-IQA) models.
  arXiv  Detail & Related papers  (2024-09-11T09:08:43Z)
• Beyond MOS: Subjective Image Quality Score Preprocessing Method Based on Perceptual Similarity [2.290956583394892]
  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.
  arXiv  Detail & Related papers  (2024-04-30T16:01:14Z)
• Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment [49.36799270585947]
  No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference. We propose a novel contrastive pre-training framework tailored for PCQA (CoPA) Our method outperforms the state-of-the-art PCQA methods on popular benchmarks.
  arXiv  Detail & Related papers  (2024-03-15T07:16:07Z)
• Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness [4.339574774938128]
  We present a novel framework for generating adversarial benchmarks to evaluate the robustness of image classification models. Our framework allows users to customize the types of distortions to be optimally applied to images, which helps address the specific distortions relevant to their deployment.
  arXiv  Detail & Related papers  (2023-10-28T07:40:42Z)
• ARNIQA: Learning Distortion Manifold for Image Quality Assessment [28.773037051085318]
  No-Reference Image Quality Assessment (NR-IQA) aims to develop methods to measure image quality in alignment with human perception without the need for a high-quality reference image. We propose a self-supervised approach named ARNIQA for modeling the image distortion manifold to obtain quality representations in an intrinsic manner.
  arXiv  Detail & Related papers  (2023-10-20T17:22:25Z)
• CONVIQT: Contrastive Video Quality Estimator [63.749184706461826]
  Perceptual video quality assessment (VQA) is an integral component of many streaming and video sharing platforms. Here we consider the problem of learning perceptually relevant video quality representations in a self-supervised manner. Our results indicate that compelling representations with perceptual bearing can be obtained using self-supervised learning.
  arXiv  Detail & Related papers  (2022-06-29T15:22:01Z)
• Paired Image-to-Image Translation Quality Assessment Using Multi-Method Fusion [0.0]
  This paper proposes a novel approach that combines signals of image quality between paired source and transformation to predict the latter's similarity with a hypothetical ground truth. We trained a Multi-Method Fusion (MMF) model via an ensemble of gradient-boosted regressors to predict Deep Image Structure and Texture Similarity (DISTS) Analysis revealed the task to be feature-constrained, introducing a trade-off at inference between metric time and prediction accuracy.
  arXiv  Detail & Related papers  (2022-05-09T11:05:15Z)
• 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)
• 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)
• Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild [98.48284827503409]
  We develop a textitunified BIQA model and an approach of training it for both synthetic and realistic distortions. We employ the fidelity loss to optimize a deep neural network for BIQA over a large number of such image pairs. Experiments on six IQA databases show the promise of the learned method in blindly assessing image quality in the laboratory and wild.
  arXiv  Detail & Related papers  (2020-05-28T13:35:23Z)

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.