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, in which a bracketed sequence of exposures are merged into an HDR image. In this thesis, we focus on a specific 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 displayed by a standard image viewer is selected as the middle exposure, 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. ResCES utilizes a patch-based process, where under-exposed, over-exposed, and motion-detected patches are stored in metadata, while other patches are reconstructed from their corresponding reference patches. This minimizes the information that needs to be stored. To further improve the fidelity of the reconstructed exposures, a residual learning model is used 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.
Citation Formats
S. SEKMEN, “COMPRESSED EXPOSURE SEQUENCES FOR HDR IMAGING,” Ph.D. - Doctoral Program, Middle East Technical University, 2024.