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UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality Assessment

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arxiv 2406.17472 v2 pith:7JZIXKYR submitted 2024-06-25 cs.CV eess.IV

Vlad Hosu , Lorenzo Agnolucci , Oliver Wiedemann , Daisuke Iso , Dietmar Saupe This is my paper · ORCID
classification cs.CVeess.IV
keywords datasetqualityimageassessmentimagesannotatedcomprisingdatabase
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce a novel Image Quality Assessment (IQA) dataset comprising 6073 UHD-1 (4K) images, annotated at a fixed width of 3840 pixels. Contrary to existing No-Reference (NR) IQA datasets, ours focuses on highly aesthetic photos of high technical quality, filling a gap in the literature. The images, carefully curated to exclude synthetic content, are sufficiently diverse to train general NR-IQA models. Importantly, the dataset is annotated with perceptual quality ratings obtained through a crowdsourcing study. Ten expert raters, comprising photographers and graphics artists, assessed each image at least twice in multiple sessions spanning several days, resulting in 20 highly reliable ratings per image. Annotators were rigorously selected based on several metrics, including self-consistency, to ensure their reliability. The dataset includes rich metadata with user and machine-generated tags from over 5,000 categories and popularity indicators such as favorites, likes, downloads, and views. With its unique characteristics, such as its focus on high-quality images, reliable crowdsourced annotations, and high annotation resolution, our dataset opens up new opportunities for advancing perceptual image quality assessment research and developing practical NR-IQA models that apply to modern photos. Our dataset is available at https://database.mmsp-kn.de/uhd-iqa-benchmark-database.html

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Cited by 3 Pith papers

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  1. Rate-Distortion Optimization with Non-Reference Metrics for UGC Compression

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    Linearizing a no-reference quality metric around the uncompressed input yields a block-wise rate-distortion cost that reduces bitrate by more than 30% versus SSE-based RDO on the metric being optimized.

  2. Image Intrinsic Scale Assessment: Bridging the Gap Between Quality and Resolution

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A new task and dataset for predicting the scale at which perceived image quality peaks, with a weak-label method that improves several no-reference quality models.

  3. UniDemoir\'e: Towards Universal Image Demoir\'eing with Data Generation and Synthesis

    cs.CV 2025-02 conditional novelty 6.0 of 10

    UniDemoiré creates large, diverse, realistic moiré training images and shows that downstream demoiréing models trained on them generalize better to new domains.

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