{"ID":2878914,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.17351","arxiv_id":"2508.17351","title":"A Hybrid Approach for Unified Image Quality Assessment: Permutation Entropy-Based Features Fused with Random Forest for Natural-Scene and Screen-Content Images for Cross-Content Applications","abstract":"Image Quality Assessment (IQA) plays a vital role in applications such as image compression, restoration, and multimedia streaming. However, existing metrics often struggle to generalize across diverse image types - particularly between natural-scene images (NSIs) and screen-content images (SCIs) - due to their differing structural and perceptual characteristics. To address this limitation, we propose a novel full-reference IQA framework: Permutation Entropy-based Features Fused with Random Forest (PEFRF). PEFRF captures structural complexity by extracting permutation entropy from the gradient maps of reference, distorted, and fused images, forming a robust feature vector. These features are then input into a Random Forest regressor trained on subjective quality scores to predict final image quality. The framework is evaluated on 13 benchmark datasets comprising over 21,000 images and 40+ state-of-the-art IQA metrics. Experimental results demonstrate that PEFRF consistently outperforms existing methods across various distortion types and content domains, establishing its effectiveness as a unified and statistically significant solution for cross-content image quality assessment.","short_abstract":"Image Quality Assessment (IQA) plays a vital role in applications such as image compression, restoration, and multimedia streaming. However, existing metrics often struggle to generalize across diverse image types - particularly between natural-scene images (NSIs) and screen-content images (SCIs) - due to their differi...","url_abs":"https://arxiv.org/abs/2508.17351","url_pdf":"https://arxiv.org/pdf/2508.17351v1","authors":"[\"Mohtashim Baqar\",\"Sian Lun Lau\",\"Mansoor Ebrahim\"]","published":"2025-08-24T13:15:41Z","proceeding":"eess.IV","tasks":"[\"eess.IV\"]","methods":"[]","has_code":false}