Centralized and distributed semi-parametric compression of piecewise smooth functions

This thesis introduces novel wavelet-based semi-parametric centralized and distributed compression methods for a class of piecewise smooth functions. Our proposed compression schemes are based on a non-conventional transform coding structure with simple independent encoders and a complex joint decoder. Current centralized state-of-the-art compression schemes are based on the conventional structure where an encoder is relatively complex and nonlinear. In addition, the setting usually allows the encoder to observe the entire source. Recently, there has been an increasing need for compression schemes where the encoder is lower in complexity and, instead, the decoder has to handle more computationally intensive tasks. Furthermore, the setup may involve multiple encoders, where each one can only partially observe the source. Such scenario is often referred to as distributed source coding. In the first part, we focus on the dual situation of the centralized compression where the encoder is linear and the decoder is nonlinear. Our analysis is centered around a class of 1-D piecewise smooth functions. We show that, by incorporating parametric estimation into the decoding procedure, it is possible to achieve the same distortion- rate performance as that of a conventional wavelet-based compression scheme. We also present a new constructive approach to parametric estimation based on the sampling results of signals with finite rate of innovation. The second part of the thesis focuses on the distributed compression scenario, where each independent encoder partially observes the 1-D piecewise smooth function. We propose a new wavelet-based distributed compression scheme that uses parametric estimation to perform joint decoding. Our distortion-rate analysis shows that it is possible for the proposed scheme to achieve that same compression performance as that of a joint encoding scheme. Lastly, we apply the proposed theoretical framework in the context of distributed image and video compression. We start by considering a simplified model of the video signal and show that we can achieve distortion-rate performance close to that of a joint encoding scheme. We then present practical compression schemes for real world signals. Our simulations confirm the improvement in performance over classical schemes, both in terms of the PSNR and the visual quality

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