JPEG AIC2026：用于图像编码细粒度评估的大规模数据集
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding
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中文总结 AI 辅助
为满足图像编码质量细粒度评估需求，介绍AIC2026数据集。从众多候选图像中选70张源图像，经多种方式筛选。涵盖多种编解码器及配置产生的伪像，对源图像编码后获大量失真图像，用多种IQA方法分析发现当前方法在细粒度质量评估上分歧大。
中文摘要 AI 辅助
传统和基于学习的图像编码的最新进展，增加了对支持压缩图像质量细粒度评估的基准数据集的需求。本文介绍了图像编码评估2026（AIC2026），这是一个用于高保真图像压缩的大规模数据集，包含70张源图像，这些图像是从2787个候选图像中通过语义聚类、客观图像质量评估（IQA）方法之间的度量分歧以及人工检查和改进选择出来的。该数据集涵盖了由八种传统编解码器和四种基于学习的编解码器在17种编码配置下产生的广泛压缩伪像。每个源图像使用七种编解码器进行编码。对于每个源 - 编解码器对，使用ColorVideoVDP（CVVDP）度量估计失真，在20个感知间隔的失真水平上提供解码图像，产生9618个失真图像。通过广泛的客观分析发现，当前IQA方法在细粒度质量差异上存在很大分歧，特别是对于基于学习的编解码器引入的伪像。完整数据集可公开获取。
英文摘要
Recent advances in conventional and learning-based image coding have increased the demand for benchmark datasets that support fine-grained assessment of compressed image quality, particularly for learning-based image compression methods. This paper introduces Assessment of Image Coding 2026 (AIC2026), a large-scale dataset for high-fidelity image compression containing 70 source images selected from 2,787 candidates using semantic clustering, inter-metric disagreement among objective image quality assessment (IQA) methods, and manual inspection and refinement. The dataset covers a wide range of compression artifacts produced by eight conventional and four learning-based codecs across 17 coding configurations. Each source image is encoded using seven codecs. For each source-codec pair, decoded images are provided at 20 perceptually spaced distortion levels, corresponding approximately to 0.2-4.0 just-noticeable difference (JND) units using the ColorVideoVDP (CVVDP) metric for distortion estimation, yielding 9,618 distorted images. This fine-grained sampling enables analysis of rate-distortion behavior and objective metric evaluation for subtle quality differences across a wide range of compression artifacts. We report an extensive objective analysis using 24 conventional and 12 learning-based IQA methods. The results show substantial disagreement among current IQA methods for fine-grained quality differences, particularly for artifacts introduced by learning-based codecs. The complete dataset is publicly available at https://doi.org/10.18419/DARUS-6156.
发表机构
- University of Konstanz（康斯坦茨大学）
- Cloudinary（Cloudinary公司）
- IST-IT（IST-IT机构）
- Fraunhofer IIS（弗劳恩霍夫应用固体物理研究所）
- Huawei（华为公司）
- Takushoku University（拓殖大学）
- IT-UBI（IT-UBI机构）
- EPFL（洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
