Biblio

The requirements of much larger file sizes, different storage formats, and immersive viewing conditions pose significant challenges to the goals of compressing VR content. At the same time, the great potential of deep learning to advance progress on the video compression problem has driven a significant research effort. Because of the high bandwidth requirements of VR, there has also been significant interest in the use of space-variant, foveated compression protocols. We have integrated these techniques to create an end-to-end deep learning video compression framework. A feature of our new compression model is that it dispenses with the need for expensive search-based motion prediction computations by using displaced frame differences. We also implement foveation in our learning based approach, by introducing a Foveation Generator Unit (FGU) that generates foveation masks which direct the allocation of bits, significantly increasing compression efficiency while making it possible to retain an impression of little to no additional visual loss given an appropriate viewing geometry. Our experiment results reveal that our new compression model, which we call the Foveated MOtionless VIdeo Codec (Foveated MOVI-Codec), is able to efficiently compress videos without computing motion, while outperforming foveated version of both H.264 and H.265 on the widely used UVG dataset and on the HEVC Standard Class B Test Sequences.

Visible light communications is an emerging architecture with unlicensed and huge bandwidth resources, security, and experimental implementations and standardization efforts. Display based transmitter and camera based receiver architectures are alternatives for device-to-device (D2D) and home area networking (HAN) systems by utilizing widely available TV, tablet and mobile phone screens as transmitters while commercially available cameras as receivers. Current architectures utilizing data hiding and unobtrusive steganography methods promise data transmission without user distraction on the screen. however, current architectures have challenges with the limited capability of data hiding in translucency or color shift based methods of hiding by uniformly distributing modulation throughout the screen and keeping eye discomfort at an acceptable level. In this article, foveation property of human visual system is utilized to define a novel modulation method denoted by FoVLC which adaptively improves data hiding capability throughout the screen based on the current eye focus point of viewer. Theoretical modeling of modulation and demodulation mechanisms hiding data in color shifts of pixel blocks is provided while experiments are performed for both FoVLC method and uniform data hiding denoted as conventional method. Experimental tests for the simple design as a proof of concept decreases average bit error rate (BER) to approximately half of the value obtained with the conventional method without user distraction while promising future efforts for optimizing block sizes and utilizing error correction codes.