Visual Importance-Biased Image Synthesis Animation

Present ray tracing algorithms are computationally intensive, requiring hours of computing time for complex scenes. Our previous work has dealt with the development of an overall approach to the application of visual attention to progressive and adaptive ray-tracing techniques. The approach facilitates large computational savings by modulating the supersampling rates in an image by the visual importance of the region being rendered. This paper extends the approach by incorporating temporal changes into the models and techniques developed, as it is expected that further efficiency savings can be reaped for animated scenes. Applications for this approach include entertainment, visualisation and simulation

Image synthesis based on a model of human vision

Modern computer graphics systems are able to construct renderings of such high quality that viewers are deceived into regarding the images as coming from a photographic source. Large amounts of computing resources are expended in this rendering process, using complex mathematical models of lighting and shading. However, psychophysical experiments have revealed that viewers only regard certain informative regions within a presented image. Furthermore, it has been shown that these visually important regions contain low-level visual feature differences that attract the attention of the viewer. This thesis will present a new approach to image synthesis that exploits these experimental findings by modulating the spatial quality of image regions by their visual importance. Efficiency gains are therefore reaped, without sacrificing much of the perceived quality of the image. Two tasks must be undertaken to achieve this goal. Firstly, the design of an appropriate region-based model of visual importance, and secondly, the modification of progressive rendering techniques to effect an importance-based rendering approach. A rule-based fuzzy logic model is presented that computes, using spatial feature differences, the relative visual importance of regions in an image. This model improves upon previous work by incorporating threshold effects induced by global feature difference distributions and by using texture concentration measures. A modified approach to progressive ray-tracing is also presented. This new approach uses the visual importance model to guide the progressive refinement of an image. In addition, this concept of visual importance has been incorporated into supersampling, texture mapping and computer animation techniques. Experimental results are presented, illustrating the efficiency gains reaped from using this method of progressive rendering. This visual importance-based rendering approach is expected to have applications in the entertainment industry, where image fidelity may be sacrificed for efficiency purposes, as long as the overall visual impression of the scene is maintained. Different aspects of the approach should find many other applications in image compression, image retrieval, progressive data transmission and active robotic vision

A Fuzzy Logic Model of Visual Importance for Efficient Image Synthesis

Experiments have shown that viewers only regard certain informative regions within a presented image. Furthermore, it has been shown that these regions of interest contain low level feature differences that influence the fixation time of the viewer. In this paper we present a novel fuzzy logic model of visual attention which seeks to compute the relative visual importance of regions in an image based upon these spatial feature differences. We also demonstrate some of the possible savings to be had in applying the visual importance model to the modulation of super-sampling in a ray-traced image. We expect this approach to have applications in the entertainment industry where image fidelity may be sacrificed for efficiency purposes

A Fuzzy Logic Model of Visual Importance for Efficient Image Synthesis

Experiments have shown that viewers only regard certain informative regions within a presented image. Furthermore, it has been shown that these regions of interest contain low level feature differences that influence the fixation time of the viewer. In this paper we present a novel fuzzy logic model of visual attention which seeks to compute the relative visual importance of regions in an image based upon these spatial feature differences. We also demonstrate some of the possible savings to be had in applying the visual importance model to the modulation of super-sampling in a ray-traced image. We expect this approach to have applications in the entertainment industry where image fidelity may be sacrificed for efficiency purposes. I