State-of-the-Art and Trends in Scalable Video Compression with Wavelet Based Approaches

3noScalable Video Coding (SVC) differs form traditional single point approaches mainly because it allows to encode in a unique bit stream several working points corresponding to different quality, picture size and frame rate. This work describes the current state-of-the-art in SVC, focusing on wavelet based motion-compensated approaches (WSVC). It reviews individual components that have been designed to address the problem over the years and how such components are typically combined to achieve meaningful WSVC architectures. Coding schemes which mainly differ from the space-time order in which the wavelet transforms operate are here compared, discussing strengths and weaknesses of the resulting implementations. An evaluation of the achievable coding performances is provided considering the reference architectures studied and developed by ISO/MPEG in its exploration on WSVC. The paper also attempts to draw a list of major differences between wavelet based solutions and the SVC standard jointly targeted by ITU and ISO/MPEG. A major emphasis is devoted to a promising WSVC solution, named STP-tool, which presents architectural similarities with respect to the SVC standard. The paper ends drawing some evolution trends for WSVC systems and giving insights on video coding applications which could benefit by a wavelet based approach.partially_openpartially_openADAMI N; SIGNORONI. A; R. LEONARDIAdami, Nicola; Signoroni, Alberto; Leonardi, Riccard

Motion Scalability for Video Coding with Flexible Spatio-Temporal Decompositions

PhDThe research presented in this thesis aims to extend the scalability range of the wavelet-based video coding systems in order to achieve fully scalable coding with a wide range of available decoding points. Since the temporal redundancy regularly comprises the main portion of the global video sequence redundancy, the techniques that can be generally termed motion decorrelation techniques have a central role in the overall compression performance. For this reason the scalable motion modelling and coding are of utmost importance, and specifically, in this thesis possible solutions are identified and analysed. The main contributions of the presented research are grouped into two interrelated and complementary topics. Firstly a flexible motion model with rateoptimised estimation technique is introduced. The proposed motion model is based on tree structures and allows high adaptability needed for layered motion coding. The flexible structure for motion compensation allows for optimisation at different stages of the adaptive spatio-temporal decomposition, which is crucial for scalable coding that targets decoding on different resolutions. By utilising an adaptive choice of wavelet filterbank, the model enables high compression based on efficient mode selection. Secondly, solutions for scalable motion modelling and coding are developed. These solutions are based on precision limiting of motion vectors and creation of a layered motion structure that describes hierarchically coded motion. The solution based on precision limiting relies on layered bit-plane coding of motion vector values. The second solution builds on recently established techniques that impose scalability on a motion structure. The new approach is based on two major improvements: the evaluation of distortion in temporal Subbands and motion search in temporal subbands that finds the optimal motion vectors for layered motion structure. Exhaustive tests on the rate-distortion performance in demanding scalable video coding scenarios show benefits of application of both developed flexible motion model and various solutions for scalable motion coding

Fast Mode Decision Algorithms for Adaptive GOP Structure in the Scalable Extension of H.264/AVC

[[abstract]]We propose a fast mode decision algorithm to reduce the computational complexity of adaptive GOP structure (AGS) in the scalable extension of H.264/AVC. AGS can improve the coding efficiency of the scalable extension of H.264. It, however, needs to perform motion-compensated temporal filtering (MCTF) of all possible GOP sizes, leading to much higher computation than the fixed GOP structure. In our proposed algorithm, after performing the MCTF with the maximum GOP size, we utilize two features to decide whether to perform the remaining MCTFs of sub-GOPs and mode selection. Experimental results show that the proposed algorithm can significantly reduce unnecessary MCTF computation for AGS, while maintaining good coding efficiency.[[fileno]]2030144030011[[department]]電機工程學

Method of Scalable Video Coding

A method of scalable video coding in which the video signal is processed and coded at various layers of spatial resolution where between each pair of spatial layers a prediction mechanism is enabled which allows a comparison at homologous layers of spatial resolution with the aim to obtain a good efficiency

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

Fully Scalable Video Coding Using Redundant-Wavelet Multihypothesis and Motion-Compensated Temporal Filtering

In this dissertation, a fully scalable video coding system is proposed. This system achieves full temporal, resolution, and fidelity scalability by combining mesh-based motion-compensated temporal filtering, multihypothesis motion compensation, and an embedded 3D wavelet-coefficient coder. The first major contribution of this work is the introduction of the redundant-wavelet multihypothesis paradigm into motion-compensated temporal filtering, which is achieved by deploying temporal filtering in the domain of a spatially redundant wavelet transform. A regular triangle mesh is used to track motion between frames, and an affine transform between mesh triangles implements motion compensation within a lifting-based temporal transform. Experimental results reveal that the incorporation of redundant-wavelet multihypothesis into mesh-based motion-compensated temporal filtering significantly improves the rate-distortion performance of the scalable coder. The second major contribution is the introduction of a sliding-window implementation of motion-compensated temporal filtering such that video sequences of arbitrarily length may be temporally filtered using a finite-length frame buffer without suffering from severe degradation at buffer boundaries. Finally, as a third major contribution, a novel 3D coder is designed for the coding of the 3D volume of coefficients resulting from the redundant-wavelet based temporal filtering. This coder employs an explicit estimate of the probability of coefficient significance to drive a nonadaptive arithmetic coder, resulting in a simple software implementation. Additionally, the coder offers the possibility of a high degree of vectorization particularly well suited to the data-parallel capabilities of modern general-purpose processors or customized hardware. Results show that the proposed coder yields nearly the same rate-distortion performance as a more complicated coefficient coder considered to be state of the art

Super Resolution of Wavelet-Encoded Images and Videos

In this dissertation, we address the multiframe super resolution reconstruction problem for wavelet-encoded images and videos. The goal of multiframe super resolution is to obtain one or more high resolution images by fusing a sequence of degraded or aliased low resolution images of the same scene. Since the low resolution images may be unaligned, a registration step is required before super resolution reconstruction. Therefore, we first explore in-band (i.e. in the wavelet-domain) image registration; then, investigate super resolution. Our motivation for analyzing the image registration and super resolution problems in the wavelet domain is the growing trend in wavelet-encoded imaging, and wavelet-encoding for image/video compression. Due to drawbacks of widely used discrete cosine transform in image and video compression, a considerable amount of literature is devoted to wavelet-based methods. However, since wavelets are shift-variant, existing methods cannot utilize wavelet subbands efficiently. In order to overcome this drawback, we establish and explore the direct relationship between the subbands under a translational shift, for image registration and super resolution. We then employ our devised in-band methodology, in a motion compensated video compression framework, to demonstrate the effective usage of wavelet subbands. Super resolution can also be used as a post-processing step in video compression in order to decrease the size of the video files to be compressed, with downsampling added as a pre-processing step. Therefore, we present a video compression scheme that utilizes super resolution to reconstruct the high frequency information lost during downsampling. In addition, super resolution is a crucial post-processing step for satellite imagery, due to the fact that it is hard to update imaging devices after a satellite is launched. Thus, we also demonstrate the usage of our devised methods in enhancing resolution of pansharpened multispectral images