Abstract
Predicting human visual perception has several applications such as compression, rendering, editing, and retargeting. Current approaches, however, ignore the fact that the human visual system compensates for geometric transformations, e.g., we see that an image and a rotated copy are identical. Instead, they will report a large, false-positive difference. At the same time, if the transformations become too strong or too spatially incoherent, comparing two images gets increasingly difficult. Between these two extrema, we propose a system to quantify the effect of transformations, not only on the perception of image differences but also on saliency and motion parallax. To this end, we first fit local homographies to a given optical flow field, and then convert this field into a field of elementary transformations, such as translation, rotation, scaling, and perspective. We conduct a perceptual experiment quantifying the increase of difficulty when compensating for elementary transformations. Transformation entropy is proposed as a measure of complexity in a flow field. This representation is then used for applications, such as comparison of nonaligned images, where transformations cause threshold elevation, detection of salient transformations, and a model of perceived motion parallax. Applications of our approach are a perceptual level-of-detail for real-time rendering and viewpoint selection based on perceived motion parallax.
Highlights
Models of human visual perception are an important component of image compression, rendering, retargeting, and editing
We propose a motion parallax measure, which approximates each of those components, in our applications we found that the linear motion parallax plays the key role
We compute transformation contrast similar to translationbased motion contrast in Ref. 54, but we perform it for all elementary transformations, and we account for neighboring homographies in a multiresolution fashion, instead considering all homographies at once
Summary
Models of human visual perception are an important component of image compression, rendering, retargeting, and editing. A typical application is prediction of differences in image pairs or detection of salient regions Such predictions are based on the perception of luminance patterns alone and ignore that a difference might be well explained by a transformation. The Hamming distance of the binary strings 1010 and 0101 is the same as between 1111 and 0000; the first pair is more similar in the sense of an edit distance, as 1010 is just a rotated, i.e., transformed version of 0101 We apply this idea to images, e.g., comparing an image and its rotated copy. We assume the optical flow[5] of an image pair to be given, either by producing it using three-dimensional (3-D) graphics or (typically with a lower precision) using computer vision techniques and focus on how the human visual system (HVS) represents transformations.
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