Book Description
The emerging Scalable Video Coding (SVC) extends the H.264/AVC video coding standard with new tools designed to efficiently support temporal, spatial and SNR scalability. In real-time multimedia systems, the coding performance of video encoders and decoders is limited by computational complexity. This thesis presents techniques to manage computational complexity of H.264/AVC and SVC video encoders. These techniques aim to provide significant complexity saving as well as a framework for efficient use of SVC. This thesis first investigates, experimentally, the computational complexity of MB coding mode decision in H.264/AVC video encoder. Based on machine learning techniques, complexity reduction algorithms are proposed. It is shown that these algorithms can reduce the computational complexity of Intra MB coding with negligible loss of video quality. Complexity reduction algorithms based on statistical classifiers are proposed for SVC encoder. It is shown that these algorithms can flexibly control the computational complexity in enhancement layers of SVC with negligible loss of video quality. The inherent relationship of MB mode decision in base and enhancement layers of SVC is investigated through experimental tests and a rate model function is proposed. An innovative fast mode decision model is developed to reduce the computational complexity by using the layer relationship along with the rate model function. We develop a general framework that applies to SVC and use this framework to adapt SVC bitstream by employing the low-complexity video encoding along with the input of video streaming constraints in order to adapt the bitstream. The proposed SVC based framework uses both objective low-complexity video encoding techniques and subjective saliency based video adaptation resulting in optimal use of network bandwidth. The approaches described in this thesis can not only reduce computational complexity of a video encoder, but also can manage the trade-off between complexity and distortion. These proposed algorithms are evaluated in terms of complexity reduction performance, rate-distortion performance and subjective and objective visual quality by experimental testing. The advantages and disadvantages of each algorithm are discussed.
