Perception-based Image Similarity Metrics

Image similarity metric is a traditional research field. Classical image processing techniques are used to design similarity metrics for all kinds of images, such as line drawings, gray or color image and even high-dynamic range (HDR) images. While existing metrics perform well for the tasks of comparing images in specified situations, few of them have systematically considered or examined the consistency with human perception required by practical applications. With the blooming of stereo devices, the similarity to be measured is not only the traditional visual difference between two images, but also the visual acceptance of two images when they are viewed simultaneously with 3D devices. This thesis presents two image similarity metrics motivated by perceptual principles, also with applications to demonstrate their novelty and practical values.;Alignment-Insensitive Shape Similarity Metric (AISS) measures shape similarity of line drawings. This metric can tolerate misalignment between two shapes and, simultaneously, accounts for the differences in transformation such as, position, orientation and scaling.;Binocular Viewing Comfort Predictor (BVCP) is another metric proposed to measure visual discomfort when human's two eyes view two different images simultaneously. According to a human vision phenomenon - binocular single vision, human vision is able to fuse two images with differences in detail, contrast and luminance, up to a certain limit. BVCP makes a first attempt in computer graphics to predict such visual comfort limit.;Applications are also proposed to evaluate AISS and BVCP. AISS is utilized in an application of Structure-based ASCII Art, which approximates line structure of the reference image content with the shapes of ASCII characters. BVCP is utilized in a novel framework - Binocular Tone Mapping which generates a binocular low-dynamic range (LDR) image pair from one HDR image. Such binocular LDR pair can be viewed with stereo devices and can preserve more human-perceivable visual content than traditional one single LDR image. Convincing results and user studies are also shown to demonstrate that both AISS and BVCP are consistent with human perception and effective in practical usage.