Steering Image Generation with Wavelet Based Perceptual Metric

It is often the case that images generated by image synthesis algorithms are judged by visual examination. The user resorts to an iterative refinement process of inspection and rendering until a satisfactory image is obtained. In this paper we propose quantitative metrics to compare images that arise from an image synthesis algorithm. The intent is to be able to guide the refinement process inherent in image synthesis. The Mean-Square-Error (MSE) has been traditionally employed to guide this process. However, it is not a viable metric for image synthesis control. We propose the use of a wavelet based perceptual metric which incorporates the frequency response of the Human Visual System. A useful aspect of the wavelet based metric is its ability to selectively measure the changes to structures of different sizes and scales in specific locations. Also, by resorting to the use of wavelets of various degrees of regularity, one can seek different levels of smoothness in an image. It is rare that such level of control can be obtained from a metric other than a wavelet based metric. We show the usefulness of our metric by examining it’s effectiveness in providing insights for common operations of an image synthesis algorithm (e.g., blurring). We also provide some examples of it’s use in rendering algorithms frequently used in graphics.