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Local SVD-Entropy Maps as a Complementary Structural Representation for Full-Reference and No-Reference Image Quality Assessment — arXiv:2609.27959

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.27959
License (as archived): CC0-1.0
Archived: 2026-10-08T12:35:32.866Z
SHA-256 of the archived text: 755883eba60e0e9a5c8d9b1b2121c0e98b502db513c7d26c456e164fdbe3c41f
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How to cite

Local SVD-Entropy Maps as a Complementary Structural Representation for Full-Reference and No-Reference Image Quality Assessment — arXiv:2609.27959 — arXiv (en), https://arxiv.org/abs/2609.27959 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f1fd545ee549 (Metraipolis archive dc_f1fd545ee549, sha256 755883eba60e0e9a5c8d9b1b2121c0e98b502db513c7d26c456e164fdbe3c41f)

BibTeX

@misc{dcf1fd545ee549,
  title = {Local SVD-Entropy Maps as a Complementary Structural Representation for Full-Reference and No-Reference Image Quality Assessment — arXiv:2609.27959},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.27959},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f1fd545ee549; sha256 755883eba60e0e9a5c8d9b1b2121c0e98b502db513c7d26c456e164fdbe3c41f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.27959. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We investigate a local spectral-complexity representation for perceptual image quality assessment (IQA) based on Shannon entropy of singular values computed directly from two-dimensional image patches. For each $3\times3$-pixel grayscale patch, SVD is applied directly and the normalized singular-value entropy defines one HSVD-map value. The construction requires neither flattening nor delay embedding, uses no boundary padding, and is invariant to $90^{\circ}$ rotations and mirror reflections at the local-descriptor level. A nested salt-and-pepper experiment on Lena separates absolute similarity to a clean reference from sensitivity to an additional degradation step. HSVD-SSIM responds more strongly to local corruption and retains a larger neighboring-state response at severe noise levels. Validation on all 10,125 distorted KADID-10k images shows that HSVD-SSIM is weaker than conventional SSIM as a standalone full-reference metric (SRCC $0.450$ vs. $0.619$), but complementary when combined with it: grouped cross-validation increases SRCC from $0.618$ to $0.659$, with a bootstrap 95\% confidence interval of $[0.036,0.046]$ for the gain. In a no-reference experiment, adding HSVD-derived single-image descriptors improves the best nonlinear model from SRCC $0.528$ to $0.575$ (95\% CI $[0.033,0.061]$) and also improves prediction of quality changes between neighboring distortion states. These results support direct local SVD entropy as an interpretable structural channel that complements conventional image-domain similarity and remains informative without a pristine reference.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-24 in cs.CV).
Categorie arXiv: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM).
Pagina del lavoro: https://arxiv.org/abs/2609.27959 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T12:12:35.849Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).