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Proceedings Paper

Analyzing the role of visual structure in the recognition of natural image content with multi-scale SSIM
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Paper Abstract

Natural images are meaningful to humans - the physical world exhibits statistical regularities that permit the human visual system (HVS) to infer useful interpretations. These regularities communicate the visual structure of the physical world and govern the statistics of images (image structure). A signal processing framework is sought to analyze image characteristics for a relationship with human interpretation. This work investigates the first step toward an objective visual information evaluation: predicting the recognition threshold of different image representations. Given a image sequence, whose images begin as unrecognizable and are gradually refined to include more information according to some measure, the recognition threshold corresponds to first the image in the sequence in which an observer accurately identifies the content. Sequences are produced using two types of image representations: signal-based and visual structure preserving. Signal-based representations add information as dictated by conventional mathematical characterizations of images based on models of low-level HVS processing and use basis functions as the basic image components. Visual structure preserving representations add information to images attributed to visual structure and attempt to mimic higher-level HVS processing by considering the scene's objects as the basic image components. An experiment is conducted to identify the recognition threshold image. Several full-reference perceptual quality assessment algorithms are evaluated in terms of their ability to predict the recognition threshold of different image representations. The cross-correlation component of a modified version of the multi-scale structural similarity (MS-SSIM) metric, denoted MS-SSIM*, exhibits a better overall correlation with the signal-based and visual structure preserving representations' average recognition thresholds than the standard MS-SSIM cross-correlation component. These findings underscore the significance of visual structure in recognition and advocate a multi-scale image structure analysis for a rudimentary evaluation of visual information.

Paper Details

Date Published: 14 February 2008
PDF: 14 pages
Proc. SPIE 6806, Human Vision and Electronic Imaging XIII, 680615 (14 February 2008); doi: 10.1117/12.768060
Show Author Affiliations
David M. Rouse, Cornell Univ. (United States)
Sheila S. Hemami, Cornell Univ. (United States)

Published in SPIE Proceedings Vol. 6806:
Human Vision and Electronic Imaging XIII
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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