Training and Testing Texture Similarity Metrics for Structurally Lossless Compression.
IEEE Trans Image Process
; 33: 1614-1626, 2024.
Article
en En
| MEDLINE
| ID: mdl-38358876
ABSTRACT
We present a systematic approach for training and testing structural texture similarity metrics (STSIMs) so that they can be used to exploit texture redundancy for structurally lossless image compression. The training and testing is based on a set of image distortions that reflect the characteristics of the perturbations present in natural texture images. We conduct empirical studies to determine the perceived similarity scale across all pairs of original and distorted textures. We then introduce a data-driven approach for training the Mahalanobis formulation of STSIM based on the resulting annotated texture pairs. Experimental results demonstrate that training results in significant improvements in metric performance. We also show that the performance of the trained STSIM metrics is competitive with state of the art metrics based on convolutional neural networks, at substantially lower computational cost.
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Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
IEEE Trans Image Process
Asunto de la revista:
INFORMATICA MEDICA
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos