Complexity management of H.264/AVC video compression.

The H. 264/AVC video coding standard offers significantly improved compression efficiency and flexibility compared to previous standards. However, the high computational complexity of H. 264/AVC is a problem for codecs running on low-power hand held devices and general purpose computers. This thesis presents new techniques to reduce, control and manage the computational complexity of an H. 264/AVC codec. A new complexity reduction algorithm for H. 264/AVC is developed. This algorithm predicts "skipped" macroblocks prior to motion estimation by estimating a Lagrange ratedistortion cost function. Complexity savings are achieved by not processing the macroblocks that are predicted as "skipped". The Lagrange multiplier is adaptively modelled as a function of the quantisation parameter and video sequence statistics. Simulation results show that this algorithm achieves significant complexity savings with a negligible loss in rate-distortion performance. The complexity reduction algorithm is further developed to achieve complexity-scalable control of the encoding process. The Lagrangian cost estimation is extended to incorporate computational complexity. A target level of complexity is maintained by using a feedback algorithm to update the Lagrange multiplier associated with complexity. Results indicate that scalable complexity control of the encoding process can be achieved whilst maintaining near optimal complexity-rate-distortion performance. A complexity management framework is proposed for maximising the perceptual quality of coded video in a real-time processing-power constrained environment. A real-time frame-level control algorithm and a per-frame complexity control algorithm are combined in order to manage the encoding process such that a high frame rate is maintained without significantly losing frame quality. Subjective evaluations show that the managed complexity approach results in higher perceptual quality compared to a reference encoder that drops frames in computationally constrained situations. These novel algorithms are likely to be useful in implementing real-time H. 264/AVC standard encoders in computationally constrained environments such as low-power mobile devices and general purpose computers

Complexity management for video encoders.

Software implementation of block-based video coding standards has been used in a wide range of applications. In many cases, such as real-time multimedia systems or power-constrained systems, the coding performance of software-only video encoders and decoders is limited by computational complexity. This thesis presents research work to develop techniques to manage computational complexity of video encoders. These techniques aim to provide significant complexity saving as well as adaptively controlling the computational complexity. This thesis first investigates experimentally the most computationally intensive functions in a video encoder. Based on the results of profile tests, several functions are selected as candidates, on which complexity reduction algorithms will be performed. These functions include discrete cosine transform and related functions as well as motion estimation. Adaptive complexity-reduction algorithms are proposed for computationally expensive functions: discrete cosine transform and motion estimation functions respectively. It is shown that these algorithms can flexibly control the computational complexity of each function with negligible loss of video quality. The inherent characteristics of coded macroblocks are investigated through experimental tests and they are categorized into "skipped" and" unskipped" macroblocks based on two parameters. An innovative algorithm is developed to reduce the computational complexity by predicting "skipped" macroblock prior to encoding and not carrying out the coding process on these macroblocks. The approaches described in this thesis can not only achieve adaptive control of the 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

Video Stream Adaptation In Computer Vision Systems

Computer Vision (CV) has been deployed recently in a wide range of applications, including surveillance and automotive industries. According to a recent report, the market for CV technologies will grow to $33.3 billion by 2019. Surveillance and automotive industries share over 20% of this market. This dissertation considers the design of real-time CV systems with live video streaming, especially those over wireless and mobile networks. Such systems include video cameras/sensors and monitoring stations. The cameras should adapt their captured videos based on the events and/or available resources and time requirement. The monitoring station receives video streams from all cameras and run CV algorithms for decisions, warnings, control, and/or other actions. Real-time CV systems have constraints in power, computational, and communicational resources. Most video adaptation techniques considered the video distortion as the primary metric. In CV systems, however, the main objective is enhancing the event/object detection/recognition/tracking accuracy. The accuracy can essentially be thought of as the quality perceived by machines, as opposed to the human perceptual quality. High-Efficiency Video Coding (HEVC) is a recent encoding standard that seeks to address the limited communication bandwidth problem as a result of the popularity of High Definition (HD) videos. Unfortunately, HEVC adopts algorithms that greatly slow down the encoding process, and thus results in complications in real-time systems. This dissertation presents a method for adapting live video streams to limited and varying network bandwidth and energy resources. It analyzes and compares the rate-accuracy and rate-energy characteristics of various video streams adaptation techniques in CV systems. We model the video capturing, encoding, and transmission aspects and then provide an overall model of the power consumed by the video cameras and/or sensors. In addition to modeling the power consumption, we model the achieved bitrate of video encoding. We validate and analyze the power consumption models of each phase as well as the aggregate power consumption model through extensive experiments. The analysis includes examining individual parameters separately and examining the impacts of changing more than one parameter at a time. For HEVC, we develop an algorithm that predicts the size of the block without iterating through the exhaustive Rate Distortion Optimization (RDO) method. We demonstrate the effectiveness of the proposed algorithm in comparison with existing algorithms. The proposed algorithm achieves approximately 5 times the encoding speed of the RDO algorithm and 1.42 times the encoding speed of the fastest analyzed algorithm

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Fast Motion Estimation Algorithms for Block-Based Video Coding Encoders

The objective of my research is reducing the complexity of video coding standards in real-time scalable and multi-view applications

No-reference bitstream-based visual quality impairment detection for high definition H.264/AVC encoded video sequences

Ensuring and maintaining adequate Quality of Experience towards end-users are key objectives for video service providers, not only for increasing customer satisfaction but also as service differentiator. However, in the case of High Definition video streaming over IP-based networks, network impairments such as packet loss can severely degrade the perceived visual quality. Several standard organizations have established a minimum set of performance objectives which should be achieved for obtaining satisfactory quality. Therefore, video service providers should continuously monitor the network and the quality of the received video streams in order to detect visual degradations. Objective video quality metrics enable automatic measurement of perceived quality. Unfortunately, the most reliable metrics require access to both the original and the received video streams which makes them inappropriate for real-time monitoring. In this article, we present a novel no-reference bitstream-based visual quality impairment detector which enables real-time detection of visual degradations caused by network impairments. By only incorporating information extracted from the encoded bitstream, network impairments are classified as visible or invisible to the end-user. Our results show that impairment visibility can be classified with a high accuracy which enables real-time validation of the existing performance objectives

A multi-objective performance optimisation framework for video coding

Digital video technologies have become an essential part of the way visual information is created, consumed and communicated. However, due to the unprecedented growth of digital video technologies, competition for bandwidth resources has become fierce. This has highlighted a critical need for optimising the performance of video encoders. However, there is a dual optimisation problem, wherein, the objective is to reduce the buffer and memory requirements while maintaining the quality of the encoded video. Additionally, through the analysis of existing video compression techniques, it was found that the operation of video encoders requires the optimisation of numerous decision parameters to achieve the best trade-offs between factors that affect visual quality; given the resource limitations arising from operational constraints such as memory and complexity. The research in this thesis has focused on optimising the performance of the H.264/AVC video encoder, a process that involved finding solutions for multiple conflicting objectives. As part of this research, an automated tool for optimising video compression to achieve an optimal trade-off between bit rate and visual quality, given maximum allowed memory and computational complexity constraints, within a diverse range of scene environments, has been developed. Moreover, the evaluation of this optimisation framework has highlighted the effectiveness of the developed solution