Perceptual indexing of visual information

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.