Human visual system model

About: Human visual system model is a research topic. Over the lifetime, 8697 publications have been published within this topic receiving 259440 citations.

A scalable wavelet-based video distortion metric and applications

Abstract: Video distortion metrics based on models of the human visual system have traditionally used comparisons between the distorted signal and a reference signal to calculate distortions objectively. In video coding applications, this is not prohibitive. In quality monitoring applications, however, access to the reference signal is often limited. This paper presents a computationally efficient video distortion metric that can operate in full- or reduced-reference mode as required. The metric is based on a model of the human visual system implemented using the wavelet transform and separable filters. The visual model is parameterized using a set of video frames and the associated quality scores. The visual model's hierarchical structure, as well as the limited impact of fine scale distortions on quality judgments of severely impaired video, are exploited to build a framework for scaling the bitrate required to represent the reference signal. Two applications of the metric are also presented. In the first, the metric is used as the distortion measure in a rate-distortion optimized rate control algorithm for MPEG-2 video compression. The resulting compressed video sequences demonstrate significant improvements in visual quality over compressed sequences with allocations determined by the TM5 rate control algorithm operating with MPEG-2 at the same rate. In the second, the metric is used to estimate time series of objective quality scores for distorted video sequences using reference bitrates as low as 10 kb/s. The resulting quality scores more accurately model subjective quality recordings than do those estimated using the mean squared error as a distortion metric, while requiring a fraction of the bitrate used to represent the reference signal. The reduced-reference metric's performance is comparable to that of the full-reference metrics tested in the first Video Quality Experts Group evaluation.

A steganography embedding method based on edge identification and XOR coding

Abstract: A method for hiding data in the spatial or IWT domains of images is proposed.Design new edge detection method to estimate same edge intensities for both images.XOR operation is used to embed the message and to improve imperceptibility.Proposed method is robust against textural feature steganalysis. In this paper, we present a novel image steganography algorithm that combines the strengths of edge detection and XOR coding, to conceal a secret message either in the spatial domain or an Integer Wavelet Transform (IWT) based transform domain of the cover image. Edge detection enables the identification of sharp edges in the cover image that when embedding in would cause less degradation to the image quality compared to embedding in a pre-specified set of pixels that do not differentiate between sharp and smooth areas. This is motivated by the fact that the human visual system (HVS) is less sensitive to changes in sharp contrast areas compared to uniform areas of the image. The edge detection method presented here is capable of estimating the exact edge intensities for both the cover and stego images (before and after embedding the message), which is essential when extracting the message. The XOR coding, on the other hand, is a simple, yet effective, process that helps in reducing differences between the cover and stego images. In order to embed three secret message bits, the algorithm requires four bits of the cover image, but due to the coding mechanism, no more than two of the four bits will be changed when producing the stego image. The proposed method utilizes the sharpest regions of the image first and then gradually moves to the less sharp regions. Experimental results demonstrate that the proposed method has achieved better imperceptibility results than other popular steganography methods. Furthermore, when applying a textural feature steganalytic algorithm to differentiate between cover and stego images produced using various embedding rates, the proposed method maintained a good level of security compared to other steganography methods.

Measurement of Visual Motion

Abstract: From the Publisher: Computer scientists designing machine vision systems, psychologists working in visual perception, visual neurophysiologists, and theoretical biologists will derive a deeper understanding of visual function - in particular the computations that the human visual system uses to analyze motion-from the important research reported in this book. The organization of movement in the changing image that reaches the eye provides our visual system with a valuable source of information for analyzing the structure of our surroundings. This book examines the measurement of this movement and the use of relative movement to locate the boundaries of physical objects in the environment. It investigates the nature of the computations that are necessary to perform this analysis by any vision system, biological or artificial. The author first defines the goals of these visual tasks, reveals the properties of the physical world that a vision system can rely upon to achieve such goals, and suggests general methods that can be used to carry out the tasks. From the general methods, she designs algorithms specifying a particular sequence of computations that a vision system can execute to perform these visual tasks. These algorithms are implemented on a computer system under a variety of circumstances. Combined with the traditional approaches of psychology and neurophysiology, this computational approach provides an exciting analysis of visual function, raising many new questions about the human vision system for further investigation. Ellen Catherine Hildreth received her doctorate from MIT. She is a Research Scientist in the MIT Artificial Intelligence Laboratory and associate director of theCenter for Biological Information Processing at the Whitaker College of Health Sciences, Technology, and Management. The Measurement of Visual Motion is an ACM Distinguished Dissertation.

Perceptual information processing system

Abstract: A system and method for perceptual processing, organization, categorization, recognition, and manipulation of visual images and visual elements. The sysstem utilizes a dynamic perceptual organization schema to adaptively drive image-processing sub-algorithms. The schema incorporates knowledge about the visual world, human perception and image categories within its structure. A fuzzy logic query control system integrates the knowledge base and image processing drivers.