Algorithms & implementation of advanced video coding standards

Advanced video coding standards have become widely deployed coding techniques used in numerous products, such as broadcast, video conference, mobile television and blu-ray disc, etc. New compression techniques are gradually included in video coding standards so that a 50% compression rate reduction is achievable every five years. However, the trend also has brought many problems, such as, dramatically increased computational complexity, co-existing multiple standards and gradually increased development time. To solve the above problems, this thesis intends to investigate efficient algorithms for the latest video coding standard, H.264/AVC. Two aspects of H.264/AVC standard are inspected in this thesis: (1) Speeding up intra4x4 prediction with parallel architecture. (2) Applying an efficient rate control algorithm based on deviation measure to intra frame. Another aim of this thesis is to work on low-complexity algorithms for MPEG-2 to H.264/AVC transcoder. Three main mapping algorithms and a computational complexity reduction algorithm are focused by this thesis: motion vector mapping, block mapping, field-frame mapping and efficient modes ranking algorithms. Finally, a new video coding framework methodology to reduce development time is examined. This thesis explores the implementation of MPEG-4 simple profile with the RVC framework. A key technique of automatically generating variable length decoder table is solved in this thesis. Moreover, another important video coding standard, DV/DVCPRO, is further modeled by RVC framework. Consequently, besides the available MPEG-4 simple profile and China audio/video standard, a new member is therefore added into the RVC framework family. A part of the research work presented in this thesis is targeted algorithms and implementation of video coding standards. In the wide topic, three main problems are investigated. The results show that the methodologies presented in this thesis are efficient and encourage

On the Information Rates of the Plenoptic Function

The {\it plenoptic function} (Adelson and Bergen, 91) describes the visual information available to an observer at any point in space and time. Samples of the plenoptic function (POF) are seen in video and in general visual content, and represent large amounts of information. In this paper we propose a stochastic model to study the compression limits of the plenoptic function. In the proposed framework, we isolate the two fundamental sources of information in the POF: the one representing the camera motion and the other representing the information complexity of the "reality" being acquired and transmitted. The sources of information are combined, generating a stochastic process that we study in detail. We first propose a model for ensembles of realities that do not change over time. The proposed model is simple in that it enables us to derive precise coding bounds in the information-theoretic sense that are sharp in a number of cases of practical interest. For this simple case of static realities and camera motion, our results indicate that coding practice is in accordance with optimal coding from an information-theoretic standpoint. The model is further extended to account for visual realities that change over time. We derive bounds on the lossless and lossy information rates for this dynamic reality model, stating conditions under which the bounds are tight. Examples with synthetic sources suggest that in the presence of scene dynamics, simple hybrid coding using motion/displacement estimation with DPCM performs considerably suboptimally relative to the true rate-distortion bound.Comment: submitted to IEEE Transactions in Information Theor

Acquisition, compression and rendering of depth and texture for multi-view video

Three-dimensional (3D) video and imaging technologies is an emerging trend in the development of digital video systems, as we presently witness the appearance of 3D displays, coding systems, and 3D camera setups. Three-dimensional multi-view video is typically obtained from a set of synchronized cameras, which are capturing the same scene from different viewpoints. This technique especially enables applications such as freeviewpoint video or 3D-TV. Free-viewpoint video applications provide the feature to interactively select and render a virtual viewpoint of the scene. A 3D experience such as for example in 3D-TV is obtained if the data representation and display enable to distinguish the relief of the scene, i.e., the depth within the scene. With 3D-TV, the depth of the scene can be perceived using a multi-view display that renders simultaneously several views of the same scene. To render these multiple views on a remote display, an efficient transmission, and thus compression of the multi-view video is necessary. However, a major problem when dealing with multiview video is the intrinsically large amount of data to be compressed, decompressed and rendered. We aim at an efficient and flexible multi-view video system, and explore three different aspects. First, we develop an algorithm for acquiring a depth signal from a multi-view setup. Second, we present efficient 3D rendering algorithms for a multi-view signal. Third, we propose coding techniques for 3D multi-view signals, based on the use of an explicit depth signal. This motivates that the thesis is divided in three parts. The first part (Chapter 3) addresses the problem of 3D multi-view video acquisition. Multi-view video acquisition refers to the task of estimating and recording a 3D geometric description of the scene. A 3D description of the scene can be represented by a so-called depth image, which can be estimated by triangulation of the corresponding pixels in the multiple views. Initially, we focus on the problem of depth estimation using two views, and present the basic geometric model that enables the triangulation of corresponding pixels across the views. Next, we review two calculation/optimization strategies for determining corresponding pixels: a local and a one-dimensional optimization strategy. Second, to generalize from the two-view case, we introduce a simple geometric model for estimating the depth using multiple views simultaneously. Based on this geometric model, we propose a new multi-view depth-estimation technique, employing a one-dimensional optimization strategy that (1) reduces the noise level in the estimated depth images and (2) enforces consistent depth images across the views. The second part (Chapter 4) details the problem of multi-view image rendering. Multi-view image rendering refers to the process of generating synthetic images using multiple views. Two different rendering techniques are initially explored: a 3D image warping and a mesh-based rendering technique. Each of these methods has its limitations and suffers from either high computational complexity or low image rendering quality. As a consequence, we present two image-based rendering algorithms that improves the balance on the aforementioned issues. First, we derive an alternative formulation of the relief texture algorithm which was extented to the geometry of multiple views. The proposed technique features two advantages: it avoids rendering artifacts ("holes") in the synthetic image and it is suitable for execution on a standard Graphics Processor Unit (GPU). Second, we propose an inverse mapping rendering technique that allows a simple and accurate re-sampling of synthetic pixels. Experimental comparisons with 3D image warping show an improvement of rendering quality of 3.8 dB for the relief texture mapping and 3.0 dB for the inverse mapping rendering technique. The third part concentrates on the compression problem of multi-view texture and depth video (Chapters 5–7). In Chapter 5, we extend the standard H.264/MPEG-4 AVC video compression algorithm for handling the compression of multi-view video. As opposed to the Multi-view Video Coding (MVC) standard that encodes only the multi-view texture data, the proposed encoder peforms the compression of both the texture and the depth multi-view sequences. The proposed extension is based on exploiting the correlation between the multiple camera views. To this end, two different approaches for predictive coding of views have been investigated: a block-based disparity-compensated prediction technique and a View Synthesis Prediction (VSP) scheme. Whereas VSP relies on an accurate depth image, the block-based disparity-compensated prediction scheme can be performed without any geometry information. Our encoder adaptively selects the most appropriate prediction scheme using a rate-distortion criterion for an optimal prediction-mode selection. We present experimental results for several texture and depth multi-view sequences, yielding a quality improvement of up to 0.6 dB for the texture and 3.2 dB for the depth, when compared to solely performing H.264/MPEG-4AVC disparitycompensated prediction. Additionally, we discuss the trade-off between the random-access to a user-selected view and the coding efficiency. Experimental results illustrating and quantifying this trade-off are provided. In Chapter 6, we focus on the compression of a depth signal. We present a novel depth image coding algorithm which concentrates on the special characteristics of depth images: smooth regions delineated by sharp edges. The algorithm models these smooth regions using parameterized piecewiselinear functions and sharp edges by a straight line, so that it is more efficient than a conventional transform-based encoder. To optimize the quality of the coding system for a given bit rate, a special global rate-distortion optimization balances the rate against the accuracy of the signal representation. For typical bit rates, i.e., between 0.01 and 0.25 bit/pixel, experiments have revealed that the coder outperforms a standard JPEG-2000 encoder by 0.6-3.0 dB. Preliminary results were published in the Proceedings of 26th Symposium on Information Theory in the Benelux. In Chapter 7, we propose a novel joint depth-texture bit-allocation algorithm for the joint compression of texture and depth images. The described algorithm combines the depth and texture Rate-Distortion (R-D) curves, to obtain a single R-D surface that allows the optimization of the joint bit-allocation in relation to the obtained rendering quality. Experimental results show an estimated gain of 1 dB compared to a compression performed without joint bit-allocation optimization. Besides this, our joint R-D model can be readily integrated into an multi-view H.264/MPEG-4 AVC coder because it yields the optimal compression setting with a limited computation effort

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Audiovisual processing for sports-video summarisation technology

In this thesis a novel audiovisual feature-based scheme is proposed for the automatic summarization of sports-video content The scope of operability of the scheme is designed to encompass the wide variety o f sports genres that come under the description ‘field-sports’. Given the assumption that, in terms of conveying the narrative of a field-sports-video, score-update events constitute the most significant moments, it is proposed that their detection should thus yield a favourable summarisation solution. To this end, a generic methodology is proposed for the automatic identification of score-update events in field-sports-video content. The scheme is based on the development of robust extractors for a set of critical features, which are shown to reliably indicate their locations. The evidence gathered by the feature extractors is combined and analysed using a Support Vector Machine (SVM), which performs the event detection process. An SVM is chosen on the basis that its underlying technology represents an implementation of the latest generation of machine learning algorithms, based on the recent advances in statistical learning. Effectively, an SVM offers a solution to optimising the classification performance of a decision hypothesis, inferred from a given set of training data. Via a learning phase that utilizes a 90-hour field-sports-video trainmg-corpus, the SVM infers a score-update event model by observing patterns in the extracted feature evidence. Using a similar but distinct 90-hour evaluation corpus, the effectiveness of this model is then tested genencally across multiple genres of fieldsports- video including soccer, rugby, field hockey, hurling, and Gaelic football. The results suggest that in terms o f the summarization task, both high event retrieval and content rejection statistics are achievable