Content-Based Group-Of-Picture Size Control in Distributed Video Coding

Controlling the group of picture (GOP) size in distributed video coding (DVC) is a difficult but important task since it has a direct impact on the coding performance. This paper presents a framework to adaptively control the size of GOPs in a Wyner-Ziv encoder by means of encoder-side decisions based on support vector machines (SVM) that uses simple features extracted from the original video content. To train the SVM, firstly this work proposes how to compute the sequence of GOP sizes with the best rate-distortion performance given the set of GOP sizes that can be used during the encoding process. Then, an algorithm based on the previously trained SVMs is presented to control the actual GOP size each time a new decision can be taken at the encoder. Results show that the proposed algorithm can achieve a rate distortion performance close to the ideal one. Moreover, comparisons with a reference adaptive GOP size selection algorithm in the literature shows gains up to 2 dB PSNR in the best condition

Dynamic Switching of GOP Configurations in High Efficiency Video Coding (HEVC) using Relational Databases for Multi-objective Optimization

Our current technological era is flooded with smart devices that provide significant computational resources that require optimal video communications solutions. Optimal and dynamic management of video bitrate, quality and energy needs to take into account their inter-dependencies. With emerging network generations providing higher bandwidth rates, there is also a growing need to communicate video with the best quality subject to the availability of resources such as computational power and available bandwidth. Similarly, for accommodating multiple users, there is a need to minimize bitrate requirements while sustaining video quality for reasonable encoding times. This thesis focuses on providing an efficient mechanism for deriving optimal solutions for High Efficiency Video Coding (HEVC) based on dynamic switching of GOP configurations. The approach provides a basic system for multi-objective optimization approach with constraints on power, video quality and bitrate. This is accomplished by utilizing a recently introduced framework known as Dynamically Reconfigurable Architectures for Time-varying Image Constraints (DRASTIC) in HEVC/H.265 encoder with six different GOP configurations to support optimization modes for minimum rate, maximum quality and minimum computational time (minimum energy in constant power configuration) mode of operation. Pareto-optimal GOP configurations are used in implementing the DRASTIC modes. Additionally, this thesis also presents a relational database formulation for supporting multiple devices that are characterized by different screen resolutions and computational resources. This approach is applicable to internet-based video streaming to different devices where the videos have been pre-compressed. Here, the video configuration modes are determined based on the application of database queries applied to relational databases. The database queries are used to retrieve a Pareto-optimal configuration based on real-time user requirements, device, and network constraints