Paper
4 January 2002 Perceptual indexing of visual information
Author Affiliations +
Proceedings Volume 4671, Visual Communications and Image Processing 2002; (2002) https://doi.org/10.1117/12.453020
Event: Electronic Imaging, 2002, San Jose, California, United States
Abstract
The application of Human perceptual models in image and video coding is motivated by the fact that non-perceptual distortion metrics (mean square error) do not correlate well with the perceived quality at lower bit-rates despite their acceptable signal to noise ratio. In this paper, we propose a novel approach for indexing the visual content of images based on human perceptual thresholds employed for encoding. In other words, the thresholds that are employed in perceptual coding also serve as an index. These thresholds depend on the overall luminance, frequency/orientation, and the variety of patterns in an image and can serve as indexing features. These features therefore have the potential to retrieve perceptually similar images in response to a query image. Detailed simulations have been carried out using the proposed indexing concept in the DCT compressed domain. Here, the indices have been computed using the DCTune coding technique, which has been shown to provide a superior visual quality in encoding images. Simulation results demonstrate that superior retrieval performance can be achieved for specific classes of images while comparable performance is obtained for other image classes.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gamal F. Fahmy and Sethuraman Panchanathan "Perceptual indexing of visual information", Proc. SPIE 4671, Visual Communications and Image Processing 2002, (4 January 2002); https://doi.org/10.1117/12.453020
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Visualization

Image retrieval

Information visualization

Image quality

Computer programming

Image compression

Quantization

RELATED CONTENT


Back to Top