HDR10+ Adaptive Ambient Compensation Using Creative Intent Metadata

Võ, Dũng Trung; Liu, Chenguang; Nelson, McClain; Mandel, Bill; Hyun, Suzie

doi:10.1109/tce.2022.3175449

Cited by 7 publications

(1 citation statement)

References 38 publications

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“…Dolby Vision [55], on the other hand, is designed for a visually immersive HDR video experience by allowing the metadata to dynamically change each frame. HDR10 [56] and HDR10+ [57] are open standards with the former being limited to static metadata and the latter supporting dynamic metadata similar to Dolby Vision.…”

Section: B Hdr Storagementioning

confidence: 99%

A Compact Multi-Exposure File Format for Backward and Forward Compatible HDR Imaging

Sekmen,

Akyüz

2024

IEEE Access

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High dynamic range (HDR) imaging techniques offer photographers the ability to capture the full range of luminance in real-world scenes, overcoming the limitations of capture and display devices. One popular method for creating HDR images is the multiple exposures technique, which involves capturing multiple exposures with regular digital cameras and combining them later to generate an HDR image. In this work, we propose a method called Residual Compressed Exposure Sequences (ResCES) that aims to consolidate all the information from a bracketed sequence into a single JPEG file. Typically, the main image that is to be displayed by a standard image viewer is selected as the middle exposure of the sequence, although any other user-preferred exposure can be selected as well. When needed, the original exposures can be reconstructed from this single JPEG file, enabling their use in a standard HDR workflow. Our proposed approach utilizes a patch-based process, where we store under-exposed, over-exposed, and motion-detected patches while reconstructing other patches through the camera response function to minimize data loss. To further improve the fidelity of the reconstructed exposures, we employ a residual learning model in the last stage of our pipeline, effectively eliminating any artifacts that may occur in its earlier stages. The key innovation of ResCES is its ability to encapsulate the complete set of original exposures within a single JPEG file in an efficient manner, allowing for on-demand reconstruction -a feature that distinguishes it from existing HDR file formats in the literature. The experimental results demonstrate that ResCES achieves a high degree of similarity with respect to the original exposures, as shown by both quantitative and qualitative evaluations. The subjective visual evaluation conducted using 40 participants indicates that ResCES reconstruction results are statistically indistinguishable from the original exposures, while, on average, yielding a 4.5 times storage reduction. This, coupled with the ease of file maintenance, simplifies storing, sharing, and viewing of HDR images.

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Section: B Hdr Storagementioning

confidence: 99%