Transformation-aware perceptual image metric

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.

Learning to Predict Image‐based Rendering Artifacts with Respect to a Hidden Reference Image

“…Transformation-invariance Surprisingly, results produced by our approach can turn out to be better than their own supervision, as our method is forced to come up with strategies to detect problems without seeing the reference. This makes it immune to a common issue of many image metrics: misalignment [KRMS16]. Even a simple shift in image content will result in many false positives for classic metrics (Fig.…”

Perception-driven Accelerated Rendering

“…For example, we can easily tell that an image is identical to its rotated copy. Kellnhofer et al [KRMS15] present a metric that quantifies the effect of transformations not only on the perception of image differences but also on saliency and motion parallax.…”