Surveillance centric coding

PhDThe research work presented in this thesis focuses on the development of techniques specific to surveillance videos for efficient video compression with higher processing speed. The Scalable Video Coding (SVC) techniques are explored to achieve higher compression efficiency. The framework of SVC is modified to support Surveillance Centric Coding (SCC). Motion estimation techniques specific to surveillance videos are proposed in order to speed up the compression process of the SCC. The main contributions of the research work presented in this thesis are divided into two groups (i) Efficient Compression and (ii) Efficient Motion Estimation. The paradigm of Surveillance Centric Coding (SCC) is introduced, in which coding aims to achieve bit-rate optimisation and adaptation of surveillance videos for storing and transmission purposes. In the proposed approach the SCC encoder communicates with the Video Content Analysis (VCA) module that detects events of interest in video captured by the CCTV. Bit-rate optimisation and adaptation are achieved by exploiting the scalability properties of the employed codec. Time segments containing events relevant to surveillance application are encoded using high spatiotemporal resolution and quality while the irrelevant portions from the surveillance standpoint are encoded at low spatio-temporal resolution and / or quality. Thanks to the scalability of the resulting compressed bit-stream, additional bit-rate adaptation is possible; for instance for the transmission purposes. Experimental evaluation showed that significant reduction in bit-rate can be achieved by the proposed approach without loss of information relevant to surveillance applications. In addition to more optimal compression strategy, novel approaches to performing efficient motion estimation specific to surveillance videos are proposed and implemented with experimental results. A real-time background subtractor is used to detect the presence of any motion activity in the sequence. Different approaches for selective motion estimation, GOP based, Frame based and Block based, are implemented. In the former, motion estimation is performed for the whole group of pictures (GOP) only when a moving object is detected for any frame of the GOP. iii While for the Frame based approach; each frame is tested for the motion activity and consequently for selective motion estimation. The selective motion estimation approach is further explored at a lower level as Block based selective motion estimation. Experimental evaluation showed that significant reduction in computational complexity can be achieved by applying the proposed strategy. In addition to selective motion estimation, a tracker based motion estimation and fast full search using multiple reference frames has been proposed for the surveillance videos. Extensive testing on different surveillance videos shows benefits of application of proposed approaches to achieve the goals of the SCC

Efficient Motion Estimation and Mode Decision Algorithms for Advanced Video Coding

H.264/AVC video compression standard achieved significant improvements in coding efficiency, but the computational complexity of the H.264/AVC encoder is drastically high. The main complexity of encoder comes from variable block size motion estimation (ME) and rate-distortion optimized (RDO) mode decision methods. This dissertation proposes three different methods to reduce computation of motion estimation. Firstly, the computation of each distortion measure is reduced by proposing a novel two step edge based partial distortion search (TS-EPDS) algorithm. In this algorithm, the entire macroblock is divided into different sub-blocks and the calculation order of partial distortion is determined based on the edge strength of the sub-blocks. Secondly, we have developed an early termination algorithm that features an adaptive threshold based on the statistical characteristics of rate-distortion (RD) cost regarding current block and previously processed blocks and modes. Thirdly, this dissertation presents a novel adaptive search area selection method by utilizing the information of the previously computed motion vector differences (MVDs). In H.264/AVC intra coding, DC mode is used to predict regions with no unified direction and the predicted pixel values are same and thus smooth varying regions are not well de-correlated. This dissertation proposes an improved DC prediction (IDCP) mode based on the distance between the predicted and reference pixels. On the other hand, using the nine prediction modes in intra 4x4 and 8x8 block units needs a lot of overhead bits. In order to reduce the number of overhead bits, an intra mode bit rate reduction method is suggested. This dissertation also proposes an enhanced algorithm to estimate the most probable mode (MPM) of each block. The MPM is derived from the prediction mode direction of neighboring blocks which have different weights according to their positions. This dissertation also suggests a fast enhanced cost function for mode decision of intra encoder. The enhanced cost function uses sum of absolute Hadamard-transformed differences (SATD) and mean absolute deviation of the residual block to estimate distortion part of the cost function. A threshold based large coefficients count is also used for estimating the bit-rate part