Building structural similarity databases for metric learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose a new approach for constructing databases for training and testing similarity metrics for structurally lossless image compression. Our focus is on structural texture similarity (STSIM) metrics and the matchedtexture compression (MTC) approach. We first discuss the metric requirements for structurally lossless compression, which differ from those of other applications such as image retrieval, classification, and understanding. We identify "interchangeability" as the key requirement for metric performance, and partition the domain of "identical" textures into three regions, of "highest," "high," and "good" similarity. We design two subjective tests for data collection, the first relies on ViSiProG to build a database of "identical" clusters, and the second builds a database of image pairs with the "highest," "high," "good," and "bad" similarity labels. The data for the subjective tests is generated during the MTC encoding process, and consist of pairs of candidate and target image blocks. The context of the surrounding image is critical for training the metrics to detect lighting discontinuities, spatial misalignments, and other border artifacts that have a noticeable effect on perceptual quality. The identical texture clusters are then used for training and testing two STSIM metrics. The labelled image pair database will be used in future research.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Human Vision and Electronic Imaging XX
EditorsBernice E. Rogowitz, Thrasyvoulos N. Pappas, Huib de Ridder
PublisherSPIE
Volume9394
ISBN (Electronic)9781628414844
DOIs
StatePublished - Jan 1 2015
EventHuman Vision and Electronic Imaging XX - San Francisco, United States
Duration: Feb 9 2015Feb 12 2015

Other

OtherHuman Vision and Electronic Imaging XX
CountryUnited States
CitySan Francisco
Period2/9/152/12/15

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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