PrISM-IQA: Image Quality Assessment Made Practical for Smartphone Photography

• URL: http://arxiv.org/abs/2606.31626v1
• Date: Tue, 30 Jun 2026 13:10:29 GMT
• Title: PrISM-IQA: Image Quality Assessment Made Practical for Smartphone Photography
• Abstract summary: We introduce a Practical ISP-aware Structured Model for IQA, which reformulates smartphone IQA as a multi-issue ordinal diagnosis problem.<n>PrISM-IQA predicts an textitordered severity level -- absent, minor, severe, or critical -- for each ISP-relevant issue.<n>We evaluate PrISM-IQA on a reconstructed SPAQ benchmark annotated with $53$ ISP-relevant quality issues and on a small-scale expert-annotated real-world dataset.
• Score: 31.119279600828282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing smartphone image quality assessment (IQA) methods commonly reduce perceptual quality to a single score. However, this scalar formulation is poorly aligned with practical image signal processor (ISP) tuning, where engineers must identify specific quality issues, estimate their severities, and determine whether they are acceptable or require intervention. In this work, we introduce a Practical ISP-aware Structured Model for IQA (PrISM-IQA), which reformulates smartphone IQA as a multi-issue ordinal diagnosis problem. Rather than regressing a single quality score, PrISM-IQA predicts an \textit{ordered} severity level -- absent, minor, severe, or critical -- for each ISP-relevant issue, covering both global image-level artifacts and local content-dependent defects. To produce logically consistent predictions, PrISM-IQA combines cumulative ordinal encoding with structured inference that captures within-issue monotonicity as well as cross-issue subsumption and exclusion relations. We evaluate PrISM-IQA on a reconstructed SPAQ benchmark annotated with $53$ ISP-relevant quality issues and on a small-scale expert-annotated real-world dataset. Experimental results demonstrate the effectiveness of PrISM-IQA for practical issue-level diagnosis, reveal transferable perceptual quality representations through linear probing, and further show how its predictions can support actionable and meaningful ISP tuning.

Related papers

• IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment [59.83941105390897]
  We propose IQA-T1, a tool-based visual evidence reasoning framework.<n>During inference, the model autonomously invokes specialized analysis tools to generate structured visual evidence.<n>Experiments show that IQA-T1 achieves the best overall performance across datasets.
  arXiv  Detail & Related papers  (2026-07-14T05:47:32Z)
• Zoom-IQA: Image Quality Assessment with Reliable Region-Aware Reasoning [32.30800226412995]
  We introduce Zoom-IQA, a VLM-based IQA model to explicitly emulate key cognitive behaviors.<n>We show that Zoom-IQA achieves improved robustness, explainability, and generalization.<n>The application to downstream tasks, such as image restoration, further demonstrates the effectiveness of Zoom-IQA.
  arXiv  Detail & Related papers  (2026-01-06T11:00:17Z)
• Life-IQA: Boosting Blind Image Quality Assessment through GCN-enhanced Layer Interaction and MoE-based Feature Decoupling [53.74410422225995]
  Blind image quality assessment (BIQA) plays a crucial role in evaluating and optimizing visual experience.<n>Most existing BIQA approaches fuse shallow and deep features extracted from backbone networks, while overlooking the unequal contributions to quality prediction.<n>This paper investigates the contributions of shallow and deep features to BIQA, and proposes a effective quality feature decoding framework via GCN-enhanced underlinelayerunderlineinteraction and MoE-based underlinefeature dunderlineecoupling, termed textbf(Life-IQA).
  arXiv  Detail & Related papers  (2025-11-24T11:59:55Z)
• AgenticIQA: An Agentic Framework for Adaptive and Interpretable Image Quality Assessment [69.06977852423564]
  Image quality assessment (IQA) reflects both the quantification and interpretation of perceptual quality rooted in the human visual system.<n>AgenticIQA decomposes IQA into four subtasks -- distortion detection, distortion analysis, tool selection, and tool execution.<n>To support training and evaluation, we introduce AgenticIQA-200K, a large-scale instruction dataset tailored for IQA agents, and AgenticIQA-Eval, the first benchmark for assessing the planning, execution, and summarization capabilities of VLM-based IQA agents.
  arXiv  Detail & Related papers  (2025-09-30T09:37:01Z)
• Computational Analysis of Degradation Modeling in Blind Panoramic Image Quality Assessment [31.49918873317235]
  Blind panoramic image quality assessment (BPIQA) has recently brought new challenge to the visual quality community.<n>Easy databases narrow the gap between the performance of BPIQA and BIQA models, which is unconducive to the development of BPIQA.
  arXiv  Detail & Related papers  (2025-03-05T08:15:31Z)
• Few-Shot Image Quality Assessment via Adaptation of Vision-Language Models [93.91086467402323]
  Gradient-Regulated Meta-Prompt IQA Framework (GRMP-IQA) designed to efficiently adapt the visual-language pre-trained model, CLIP, to IQA tasks.<n> GRMP-IQA consists of two core modules: (i) Meta-Prompt Pre-training Module and (ii) Quality-Aware Gradient Regularization.
  arXiv  Detail & Related papers  (2024-09-09T07:26:21Z)
• DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild [73.6767681305851]
  Blind image quality assessment (IQA) in the wild presents significant challenges.<n>Given the difficulty in collecting large-scale training data, leveraging limited data to develop a model with strong generalization remains an open problem.<n>Motivated by the robust image perception capabilities of pre-trained text-to-image (T2I) diffusion models, we propose a novel IQA method, diffusion priors-based IQA.
  arXiv  Detail & Related papers  (2024-05-30T12:32:35Z)
• Cross-IQA: Unsupervised Learning for Image Quality Assessment [3.2287957986061038]
  We propose a no-reference image quality assessment (NR-IQA) method termed Cross-IQA based on vision transformer(ViT) model. The proposed Cross-IQA method can learn image quality features from unlabeled image data. Experimental results show that Cross-IQA can achieve state-of-the-art performance in assessing the low-frequency degradation information.
  arXiv  Detail & Related papers  (2024-05-07T13:35:51Z)
• 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.<n>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)
• Blind Multimodal Quality Assessment: A Brief Survey and A Case Study of Low-light Images [73.27643795557778]
  Blind image quality assessment (BIQA) aims at automatically and accurately forecasting objective scores for visual signals. Recent developments in this field are dominated by unimodal solutions inconsistent with human subjective rating patterns. We present a unique blind multimodal quality assessment (BMQA) of low-light images from subjective evaluation to objective score.
  arXiv  Detail & Related papers  (2023-03-18T09:04:55Z)
• Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment [20.288424566444224]
  We explore normalization in the design of loss functions for image quality assessment (IQA) models. The resulting "Norm-in-Norm'' loss encourages the IQA model to make linear predictions with respect to subjective quality scores. Experiments on two relevant datasets show that, compared to MAE or MSE loss, the new loss enables the IQA model to converge about 10 times faster.
  arXiv  Detail & Related papers  (2020-08-10T04:01:21Z)

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.