Evaluation of reverse tone mapping through varying exposure conditions

Abstract

Most existing image content has low dynamic range (LDR), which necessitates effective methods to display such legacy content on high dynamic range (HDR) devices. Reverse tone mapping operators (rTMOs) aim to take LDR content as input and adjust the contrast intelligently to yield output that recreates the HDR experience. In this paper we show that current rTMO approaches fall short when the input image is not exposed properly. More specifically, we report a series of perceptual experiments using a Brightside HDR display and show that, while existing rTMOs perform well for under-exposed input data, the perceived quality degrades substantially with over-exposure, to the extent that in some cases subjects prefer the LDR originals to images that have been treated with rTMOs. We show that, in these cases, a simple rTMO based on gamma expansion avoids the errors introduced by other methods, and propose a method to automatically set a suitable gamma value for each image, based on the image key and empirical data. We validate the results both by means of perceptual experiments and using a recent image quality metric, and show that this approach enhances visible details without causing artifacts in incorrectlyexposed regions. Additionally, we perform another set of experiments which suggest that spatial artifacts introduced by rTMOs are more disturbing than inaccuracies in the expanded intensities. Together, these findings suggest that when the quality of the input data is unknown, reverse tone mapping should be handled with simple, non-aggressive methods to achieve the desired effect.

Dynamic range expansion of video sequences: A subjective quality assessment study

“…However, the provided content needs to be correctly exposed. In the case of overexposed LDR content (i.e., large saturated areas), a non-linear expansion (e.g., adaptive gamma) needs to be employed to avoid loss of visual contrast [13].…”

“…Masia et al [13] considered the effect of the exposure of the LDR input on the produced HDR picture. The tested EOs include [2,3] and linear expansion [11].…”

“…Few works in the literature have compared the performance of EOs [11][12][13]. Assessing the quality of expanded HDR content is not simply a matter of fidelity to the original LDR, but entails complex and challenging aesthetic considerations, difficult to model in an objective quality metric.…”

High‐dynamic range image generation from single low‐dynamic range image

“…Then, Akyuz et al [8] performed two experiments which reveal that in many circumstances a linear contrast scaling works surprisingly well for mapping LDR content onto HDR screens, suggest that simple solutions may suffice for LDR expansion. Afterwards, Masia et al [3] presented a simple expansion method based on g transformation, which expands the whole image with a same g value. Meylan et al [9,10] classified image into specular highlight component and diffuse component, applying a steep linear tone mapping curve to the highlight component and a mild linear curve to the diffuse component.…”

“…Most of the existing image expansion methods make a general assumption that highly saturated pixels need to be expanded much more than the rest. As a result, bright image areas representing features such as highlights, or the sun in the sky, are largely boosted, thus sometimes results in contouring artefacts for bright objects [3]. On the other hand, in some applications, such as photography, movie, video, object segmentation and recognition, highlight makes image unnatural and leads to false results.…”