High Dynamic Range
High dynamic range (HDR) imaging aims to capture and reproduce the full range of light intensities present in a scene, exceeding the limitations of standard cameras. Current research focuses on developing efficient algorithms and neural network architectures, such as diffusion models and Gaussian splatting, to reconstruct HDR images and videos from multiple low dynamic range (LDR) exposures or single LDR images, often incorporating techniques like tone mapping and exposure completion to address challenges such as ghosting and limited dynamic range. These advancements are significant for improving image and video quality in various applications, including computer vision, virtual and augmented reality, and advanced driver-assistance systems.
Papers
HDR or SDR? A Subjective and Objective Study of Scaled and Compressed Videos
Joshua P. Ebenezer, Zaixi Shang, Yixu Chen, Yongjun Wu, Hai Wei, Sriram Sethuraman, Alan C. Bovik
HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos
Joshua P. Ebenezer, Zaixi Shang, Yongjun Wu, Hai Wei, Sriram Sethuraman, Alan C. Bovik
Making Video Quality Assessment Models Robust to Bit Depth
Joshua P. Ebenezer, Zaixi Shang, Yongjun Wu, Hai Wei, Sriram Sethuraman, Alan C. Bovik