A High Resolution Multi-exposure Stereoscopic Image & Video Database of Natural Scenes
Rohit Choudhary, Mansi Sharma, Aditya Wadaskar

TL;DR
This paper introduces a comprehensive high-resolution multi-exposure stereoscopic dataset of natural scenes, capturing diverse outdoor and indoor environments to support HDR imaging, depth estimation, and 3D HDR research.
Contribution
The paper presents a new, publicly available multi-exposure stereoscopic dataset with detailed capture, alignment, and calibration procedures, addressing a critical data gap in 3D HDR video research.
Findings
Dataset includes diverse outdoor and indoor scenes with complex depth and motion
Provides detailed methodology for capturing and calibrating multi-exposure stereo data
Facilitates research in HDR imaging, depth estimation, and 3D HDR coding
Abstract
Immersive displays such as VR headsets, AR glasses, Multiview displays, Free point televisions have emerged as a new class of display technologies in recent years, offering a better visual experience and viewer engagement as compared to conventional displays. With the evolution of 3D video and display technologies, the consumer market for High Dynamic Range (HDR) cameras and displays is quickly growing. The lack of appropriate experimental data is a critical hindrance for the development of primary research efforts in the field of 3D HDR video technology. Also, the unavailability of sufficient real world multi-exposure experimental dataset is a major bottleneck for HDR imaging research, thereby limiting the quality of experience (QoE) for the viewers. In this paper, we introduce a diversified stereoscopic multi-exposure dataset captured within the campus of Indian Institute of…
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Taxonomy
TopicsImage Enhancement Techniques · Advanced Vision and Imaging · Advanced Image Processing Techniques
