The main goal of video coding algorithms is to achieve high compression efficiency while maintaining quality of the compressed signal at the highest level. Human visual system is the ultimate receiver of compressed signal and final judge of its quality. This dissertation presents work towards optimal video compression algorithm that is based on the characteristics of our visual system. Modeling phenomena such as backward temporal masking and motion masking we developed algorithms that are implemented in the state-of- the-art video encoders. Result of using our algorithms is visually lossless compression with improved efficiency, as verified by standard subjective quality and psychophysical tests. Savings in bitrate compared to the High Efficiency Video Coding / H.265 reference implementation are up to 45%. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Recently, multimedia applications and their use have grown dramatically in popularity in strong part due to mobile device adoption by the consumer market. Applications, such as video conferencing, have gained popularity. These applications and others have a strong video component that uses the mobile device’s resources. These resources include processing time, network bandwidth, memory use, and battery life. The goal is to reduce the need of these resources by reducing the complexity of the coding process. Mobile devices offer unique characteristics that can be exploited for optimizing video codecs. The combination of small display size, video resolution, and human vision factors, such as acuity, allow encoder optimizations that will not (or minimally) impact subjective quality. The focus of this dissertation is optimizing video services in mobile environments. Industry has begun migrating from H.264 video coding to a more resource intensive but compression efficient High Efficiency Video Coding (HEVC). However, there has been no proper evaluation and optimization of HEVC for mobile environments. Subjective quality evaluations were performed to assess relative quality between H.264 and HEVC. This will allow for better use of device resources and migration to new codecs where it is most useful. Complexity of HEVC is a significant barrier to adoption on mobile devices and complexity reduction methods are necessary. Optimal use of encoding options is needed to maximize quality and compression while minimizing encoding time. Methods for optimizing coding mode selection for HEVC were developed. Complexity of HEVC encoding can be further reduced by exploiting the mismatch between the resolution of the video, resolution of the mobile display, and the ability of the human eyes to acquire and process video under these conditions. The perceptual optimizations developed in this dissertation use the properties of spatial (visual acuity) and temporal information processing (motion perception) to reduce the complexity of HEVC encoding. A unique feature of the proposed methods is that they reduce encoding complexity and encoding time. The proposed HEVC encoder optimization methods reduced encoding time by 21.7% and bitrate by 13.4% with insignificant impact on subjective quality evaluations. These methods can easily be implemented today within HEVC. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection