EyeSim-VQA: A Free-Energy-Guided Eye Simulation Framework for Video Quality Assessment

• URL: http://arxiv.org/abs/2506.11549v1
• Date: Fri, 13 Jun 2025 08:00:54 GMT
• Title: EyeSim-VQA: A Free-Energy-Guided Eye Simulation Framework for Video Quality Assessment
• Authors: Zhaoyang Wang, Wen Lu, Jie Li, Lihuo He, Maoguo Gong, Xinbo Gao,
• Abstract summary: EyeSimVQA is a novel VQA framework that incorporates free-energy-based self-repair.<n>We show EyeSimVQA achieves competitive or superior performance compared to state-of-the-art methods.
• Score: 68.77813885751308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Free-energy-guided self-repair mechanisms have shown promising results in image quality assessment (IQA), but remain under-explored in video quality assessment (VQA), where temporal dynamics and model constraints pose unique challenges. Unlike static images, video content exhibits richer spatiotemporal complexity, making perceptual restoration more difficult. Moreover, VQA systems often rely on pre-trained backbones, which limits the direct integration of enhancement modules without affecting model stability. To address these issues, we propose EyeSimVQA, a novel VQA framework that incorporates free-energy-based self-repair. It adopts a dual-branch architecture, with an aesthetic branch for global perceptual evaluation and a technical branch for fine-grained structural and semantic analysis. Each branch integrates specialized enhancement modules tailored to distinct visual inputs-resized full-frame images and patch-based fragments-to simulate adaptive repair behaviors. We also explore a principled strategy for incorporating high-level visual features without disrupting the original backbone. In addition, we design a biologically inspired prediction head that models sweeping gaze dynamics to better fuse global and local representations for quality prediction. Experiments on five public VQA benchmarks demonstrate that EyeSimVQA achieves competitive or superior performance compared to state-of-the-art methods, while offering improved interpretability through its biologically grounded design.

Related papers

• DGIQA: Depth-guided Feature Attention and Refinement for Generalizable Image Quality Assessment [9.851063768646847]
  A long-held challenge in no-reference image quality assessment is the lack of objective generalization to unseen natural distortions.<n>We integrate a novel Depth-Guided cross-attention and refinement mechanism, which distills scene depth and spatial features into a structure-aware representation.<n>We implement TCB and Depth-CAR as multimodal attention-based projection functions to select the most informative features.<n> Experimental results demonstrate that our proposed DGIQA model achieves state-of-the-art (SOTA) performance on both synthetic and authentic benchmark datasets.
  arXiv  Detail & Related papers  (2025-05-29T20:52:56Z)
• Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-Supervision [49.46606936180063]
  Video quality assessment (VQA) is essential for quantifying quality in various video processing systems.<n>We introduce a self-supervised learning framework for VQA to learn quality assessment capabilities from large-scale, unlabeled web videos.<n>By training on a dataset $10times$ larger than the existing VQA benchmarks, our model achieves zero-shot performance.
  arXiv  Detail & Related papers  (2025-05-06T15:29:32Z)
• InvFussion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems [76.39776789410088]
  This work introduces a framework that combines the strong performance of supervised approaches and the flexibility of zero-shot methods.<n>A novel architectural design seamlessly integrates the degradation operator directly into the denoiser.<n> Experimental results on the FFHQ and ImageNet datasets demonstrate state-of-the-art posterior-sampling performance.
  arXiv  Detail & Related papers  (2025-04-02T12:40:57Z)
• Q-Insight: Understanding Image Quality via Visual Reinforcement Learning [27.26829134776367]
  Image quality assessment (IQA) focuses on the perceptual visual quality of images, playing a crucial role in downstream tasks such as image reconstruction, compression, and generation.<n>We propose Q-Insight, a reinforcement learning-based model built upon group relative policy optimization (GRPO)<n>We show that Q-Insight substantially outperforms existing state-of-the-art methods in both score regression and degradation perception tasks.
  arXiv  Detail & Related papers  (2025-03-28T17:59:54Z)
• IQPFR: An Image Quality Prior for Blind Face Restoration and Beyond [56.99331967165238]
  Blind Face Restoration (BFR) addresses the challenge of reconstructing degraded low-quality (LQ) facial images into high-quality (HQ) outputs.<n>We propose a novel framework that incorporates an Image Quality Prior (IQP) derived from No-Reference Image Quality Assessment (NR-IQA) models.<n>Our method outperforms state-of-the-art techniques across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-03-12T11:39:51Z)
• Large Multi-modality Model Assisted AI-Generated Image Quality Assessment [53.182136445844904]
  We introduce a large Multi-modality model Assisted AI-Generated Image Quality Assessment (MA-AGIQA) model. It uses semantically informed guidance to sense semantic information and extract semantic vectors through carefully designed text prompts. It achieves state-of-the-art performance, and demonstrates its superior generalization capabilities on assessing the quality of AI-generated images.
  arXiv  Detail & Related papers  (2024-04-27T02:40:36Z)
• Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models [71.06007696593704]
  Blind quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users' viewing experience in real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. We conduct a first-of-its-kind computational analysis of VQA datasets via minimalistic BVQA models.
  arXiv  Detail & Related papers  (2023-07-26T06:38:33Z)
• Visual Mechanisms Inspired Efficient Transformers for Image and Video Quality Assessment [5.584060970507507]
  Perceptual mechanisms in the human visual system play a crucial role in the generation of quality perception. This paper proposes a general framework for no-reference visual quality assessment using efficient windowed transformer architectures.
  arXiv  Detail & Related papers  (2022-03-28T07:55:11Z)

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.