A Novel Light Field Coding Scheme Based on Deep Belief Network & Weighted Binary Images for Additive Layered Displays
Sally Khaidem, Mansi Sharma

TL;DR
This paper introduces a new light field coding framework using Deep Belief Networks and weighted binary images, enabling scalable, high-quality light field representation for augmented reality displays.
Contribution
It presents a novel light field coding scheme combining DBN and weighted binary images, optimized for additive layered AR displays with adaptive bitrate capabilities.
Findings
Effective capture of light field redundancies
Achieves scalable bitrate encoding with H.265
Validated on real and synthetic datasets
Abstract
Light-field displays create an immersive experience by providing binocular depth sensation and motion parallax. Stacking light attenuating layers is one approach to implement a light field display with a broader depth of field, wide viewing angles and high resolution. Due to the transparent holographic optical element (HOE) layers, additive layered displays can be integrated into augmented reality (AR) wearables to overlay virtual objects onto the real world, creating a seamless mixed reality (XR) experience. This paper proposes a novel framework for light field representation and coding that utilizes Deep Belief Network (DBN) and weighted binary images suitable for additive layered displays. The weighted binary representation of layers makes the framework more flexible for adaptive bitrate encoding. The framework effectively captures intrinsic redundancies in the light field data, and…
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Taxonomy
TopicsAdvanced Vision and Imaging · Advanced Optical Imaging Technologies · Image Enhancement Techniques
MethodsDeep Belief Network
