Human visual system for evaluation of holographic image quality

In this paper, we present a unified framework for evaluating the visual quality of holographic images based on Human Visual System (HVS). Analyzing what holographic image and its three-dimensional features really look like to the human eye is the purpose of this study. By exploiting schematic eye based on HVS, we focus on the tracing of lights that is emitted from holographically reconstructed image and propagates through intraocular structure of the human eye. In particular, we perform, based on wave theory, numerical simulation that aims at complex wave-field distribution of intraocular lights, to effectively deal with holographic properties

Improving Holographic Search Algorithms using Sorted Pixel Selection

Traditional search algorithms for computer hologram generation such as Direct Search and Simulated Annealing offer some of the best hologram qualities at convergence when compared to rival approaches. Their slow generation times and high processing power requirements mean, however, that they see little use in performance critical applications. This paper presents the novel Sorted Pixel Selection (SPS) modification for Holographic Search Algorithms (HSAs) that offers Mean Square Error (MSE) reductions in the range of 14:7 19:2% for the test images used. SPS operates by substituting a weighted search selection procedure for traditional random pixel selection processes. While small, the improvements seen are observed consistently across a wide range of test cases and require limited overhead for implementatio

Cost-optimized heterogeneous FPGA architecture for non-iterative hologram generation.

The generation of computer-generated holograms (CGHs) requires a significant amount of computational power. To accelerate the process, highly parallel field-programmable gate arrays (FPGAs) are deemed to be a promising computing platform to implement non-iterative hologram generation algorithms. In this paper, we present a cost-optimized heterogeneous FPGA architecture based on a one-step phase retrieval algorithm for CGH generation. The results indicate that our hardware implementation is 2.5× faster than the equivalent software implementation on a personal computer with a high-end multi-core CPU. Trade-offs between cost and performance are demonstrated, and we show that the proposed heterogeneous architecture can be used in a compact display system that is cost and size optimized

Optimizing vision and visuals: lectures on cameras, displays and perception

The evolution of the internet is underway, where immersive virtual 3D environments (commonly known as metaverse or telelife) will replace flat 2D interfaces. Crucial ingredients in this transformation are next-generation displays and cameras representing genuinely 3D visuals while meeting the human visual system's perceptual requirements. This course will provide a fast-paced introduction to optimization methods for next-generation interfaces geared towards immersive virtual 3D environments. Firstly, we will introduce lensless cameras for high dimensional compressive sensing (e.g., single exposure capture to a video or one-shot 3D). Our audience will learn to process images from a lensless camera at the end. Secondly, we introduce holographic displays as a potential candidate for next-generation displays. By the end of this course, you will learn to create your 3D images that can be viewed using a standard holographic display. Lastly, we will introduce perceptual guidance that could be an integral part of the optimization routines of displays and cameras. Our audience will gather experience in integrating perception to display and camera optimizations. This course targets a wide range of audiences, from domain experts to newcomers. To do so, examples from this course will be based on our in-house toolkit to be replicable for future use. The course material will provide example codes and a broad survey with crucial information on cameras, displays and perception

Compression of phase-only holograms with JPEG standard and deep learning

It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed "JPEG + deep learning" hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression