Originally presented as the author's thesis--Universität Hannover. / Includes bibliographical references.

Αντικείμενο της διπλωματικής εργασίας αποτελεί η κλιμακοθετήσιμη κωδικοποίηση βίντεο (scalable video coding) με χρήση του μετασχηματισμού wavelet. Η κλιμακοθετήσιμη κωδικοποίηση βίντεο αποτελεί ένα πλαίσιο εργασίας, όπου από μια ενιαία συμπιεσμένη ακολουθία βίντεο μπορούν να προκύψουν αναπαραστάσεις του βίντεο με διαφορετική ποιότητα, ανάλυση και ρυθμό πλαισίων. Η κλιμακοθετησιμότητα του βίντεο αποτελεί σημαντική ιδιότητα ενός συστήματος στις μέρες μας, όπου το video-streaming και η επικοινωνία με βίντεο γίνεται μέσω μη αξιόπιστων μέσων διάδοσης και μεταξύ τερματικών με διαφορετικές δυνατότητες Στην εργασία αυτή αρχικά μελετάται ο μετασχηματισμός wavelet, ο οποίος αποτελεί το βασικό εργαλείο για την κλιμακοθετήσιμη κωδικοποίηση τόσο εικόνων όσο και ακολουθιών βίντεο. Στην συνέχεια, αναλύουμε την ιδέα της ανάλυσης πολλαπλής διακριτικής ικανότητας (multiresolution analysis) και την υλοποίηση του μετασχηματισμού wavelet με χρήση του σχήματος ανόρθωσης (lifting scheme), η οποία προκάλεσε νέο ενδιαφέρον στο χώρο της κλιμακοθετήσιμης κωδικοποίησης βίντεο. Τα κλιμακοθετήσιμα συστήματα κωδικοποίησης βίντεο διακρίνονται σε δύο κατηγορίες: σε αυτά που εφαρμόζουν το μετασχηματισμό wavelet πρώτα στο πεδίο του χρόνου και έπειτα στο πεδίο του χώρου και σε αυτά που εφαρμόζουν το μετασχηματισμό wavelet πρώτα στο πεδίο του χώρου και έπειτα στο πεδίο του χρόνου. Εμείς εστιάzουμε στη πρώτη κατηγορία και αναλύουμε τη διαδικάσια κλιμακοθετήσιμης κωδικοποίησης/αποκωδικοποίησης καθώς και τα επιμέρους κομμάτια από τα οποία αποτελείται. Τέλος, εξετάζουμε τον τρόπο με τον οποίο διάφορες παράμετρoι επηρεάζουν την απόδοση ενός συστήματος κλιμακοθετήσιμης κωδικοποίησης βίντεο και παρουσιάζουμε τα αποτελέσματα από τις πειραματικές μετρήσεις. Βασιζόμενοι στα πειραματικά αποτελέσματα προτείνουμε έναν προσαρμοστικό τρόπο επιλογής των παραμέτρων με σκοπό τη βελτίωση της απόδοσης και συγχρόνως τη μείωση της πολυπλοκότητας. / In this master thesis we examine the scalable video coding based on the wavelet transform. Scalable video coding refers to a compression framework where content representations with different quality, resolution, and frame-rate can be extracted from parts of one compressed bitstream. Scalable video coding based on motion-compensated spatiotemporal wavelet decompositions is becoming increasingly popular, as it provides coding performance competitive with state-of-the-art coders, while trying to accommodate varying network bandwidths and different receiver capabilities (frame-rate, display size, CPU, etc.) and to provide solutions for network congestion or video server design. In this master thesis we investigate the wavelet transform, the multiresolution analysis and the lifting scheme. Then, we focus on the scalable video coding/decoding. There exist two different architectures of scalable video coding. The first one performs the wavelet transform firstly on the temporal direction and then performs the spatial wavelet decomposition. The other architecture performs firstly the spatial wavelet transform and then the temporal decomposition. We focus on the first architecture, also known as t+2D scalable coding systems. Several coding parameters affect the performance of the scalable video coding scheme such as the number of temporal levels and the interpolation filter used for subpixel accuracy. We have conducted extensive experiments in order to test the influence of these parameters. The influence of these parameters proves to be dependent on the video content. Thus, we present an adaptive way of choosing the value of these parameters based on the video content. Experimental results show that the proposed method not only significantly improves the performance but reduces the complexity of the coding procedure.

In this thesis, we study the concept of scalable video coding as implemented in the extension to the H.264 video coding standard. Specifically, for the spatial and quality scalability scenarios, we propose an optimization algorithm based on the Soft Decision Quantization (SDQ) concept, which aims at jointly optimizing all layers being encoded. The performance of the algorithm was assessed in an SVC implementation. Experimental results show, that the proposed method significantly improves the coding efficiency when compared to an unmodified SVC encoder.

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To push the envelope of DCT-based lossy image/video compression, this thesis is motivated to revisit design of some fundamental blocks in image/video coding, ranging from source modelling, quantization table, quantizers, to entropy coding. Firstly, to better handle the heavy tail phenomenon commonly seen in DCT coefficients, a new model dubbed transparent composite model (TCM) is developed and justified. Given a sequence of DCT coefficients, the TCM first separates the tail from the main body of the sequence, and then uses a uniform distribution to model DCT coefficients in the heavy tail, while using a parametric distribution to model DCT coefficients in the main body. The separation boundary and other distribution parameters are estimated online via maximum likelihood (ML) estimation. Efficient online algorithms are proposed for parameter estimation and their convergence is also proved. When the parametric distribution is truncated Laplacian, the resulting TCM dubbed Laplacian TCM (LPTCM) not only achieves superior modeling accuracy with low estimation complexity, but also has a good capability of nonlinear data reduction by identifying and separating a DCT coefficient in the heavy tail (referred to as an outlier) from a DCT coefficient in the main body (referred to as an inlier). This in turn opens up opportunities for it to be used in DCT-based image compression. Secondly, quantization table design is revisited for image/video coding where soft decision quantization (SDQ) is considered. Unlike conventional approaches where quantization table design is bundled with a specific encoding method, we assume optimal SDQ encoding and design a quantization table for the purpose of reconstruction. Under this assumption, we model transform coefficients across different frequencies as independently distributed random sources and apply the Shannon lower bound to approximate the rate distortion function of each source. We then show that a quantization table can be optimized in a way that the resulting distortion complies with certain behavior, yielding the so-called optimal distortion profile scheme (OptD). Guided by this new theoretical result, we present an efficient statistical-model-based algorithm using the Laplacian model to design quantization tables for DCT-based image compression. When applied to standard JPEG encoding, it provides more than 1.5 dB performance gain (in PSNR), with almost no extra burden on complexity. Compared with the state-of-the-art JPEG quantization table optimizer, the proposed algorithm offers an average 0.5 dB gain with computational complexity reduced by a factor of more than 2000 when SDQ is off, and a 0.1 dB performance gain or more with 85% of the complexity reduced when SDQ is on. Thirdly, based on the LPTCM and OptD, we further propose an efficient non-predictive DCT-based image compression system, where the quantizers and entropy coding are completely re-designed, and the relative SDQ algorithm is also developed. The proposed system achieves overall coding results that are among the best and similar to those of H.264 or HEVC intra (predictive) coding, in terms of rate vs visual quality. On the other hand, in terms of rate vs objective quality, it significantly outperforms baseline JPEG by more than 4.3 dB on average, with a moderate increase on complexity, and ECEB, the state-of-the-art non-predictive image coding, by 0.75 dB when SDQ is off, with the same level of computational complexity, and by 1 dB when SDQ is on, at the cost of extra complexity. In comparison with H.264 intra coding, our system provides an overall 0.4 dB gain or so, with dramatically reduced computational complexity. It offers comparable or even better coding performance than HEVC intra coding in the high-rate region or for complicated images, but with only less than 5% of the encoding complexity of the latter. In addition, our proposed DCT-based image compression system also offers a multiresolution capability, which, together with its comparatively high coding efficiency and low complexity, makes it a good alternative for real-time image processing applications.

Achieving high compression ratio without significant loss of quality is the main goal of the most standard video coding systems. Since consecutive frames of a general video sequence have high correlations, the temporal redundancy between frames is removed by using motion estimation and motion compensation techniques. In this thesis, we investigate the use of video content information within the video coding system and propose a new video coding approach that can save significant bit-rates of the compressed video. Main units of the proposed coding scheme include the scene analyzer and image interpolator. The scene analyzer at the encoder extracts scene-modeling parameters from input sequences. The image interpolator at the decoder reconstructs the video frames by using the transmitted modeling parameters. The scene analyzer consists of the camera motion detector and image-matching module. We propose a new camera motion detection method that directly analyzes the 2-D distribution of inter-frame motion fields. Experimental results show that the proposed method provides higher detection accuracy and faster computation time than the 1-D angle histogram-based method. A robust image-matching method that is invariant to scale changes, rotations, and illumination changes has been presented. Invariance to these changes is achieved by adopting mutual information as a measure of similarity and adaptively changing the size and orientation of the local matching windows. To reduce ambiguities of the local matching, a global matching technique has been combined with the local matching. To evaluate the performance of the proposed coding scheme, we have integrated the camera motion detector, the image-matching module, and the image interpolator with the standard MPEG-4 video codec. We compare our method with the standard MPEG-4 codec in terms of bit rates, computation time, and subjective and objective qualities.

Fourth Generation (4G) cellular technology Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) offers high data rate capabilities to mobile users; and, operators are trying to deliver a true mobile broadband experience over LTE networks. Mobile TV and Video on Demand (VoD) are expected to be the main revenue generators in the near future [36] and efficient video streaming over wireless is the key to enabling this. 3GPP recommends the use of H.264 baseline profiles for all video based services in Third Generation (3G) Universal Mobile Telecommunication System (UMTS) networks. However, LTE networks need to support mobile devices with different display resolution requirements like small resolution mobile phones and high resolution laptops. Scalable Video Coding (SVC) is required to achieve this goal. Feasibility study of SVC for LTE is one of the main agenda of 3GPP Release10. SVC enhances H.264 with a set of new profiles and encoding tools that may be used to produce scalable bit streams. Efficient adaptation methods for SVC video transmission over LTE networks are proposed in this thesis. Advantages of SVC over H.264 are analyzed using real time use cases of mobile video streaming. Further, we study the cross layer adaptation and scheduling schemes for delivering SVC video streams most efficiently to the users in LTE networks in unicast and multicast transmissions. We propose SVC based video streaming scheme for unicast and multicast transmissions in the downlink direction, with dynamic adaptations and a scheduling scheme based on channel quality information from users. Simulation results indicate improved video quality for more number of users in the coverage area and efficient spectrum usage with the proposed methods.

Video compression is an essential component of present-day applications and a decisive factor between the success or failure of a business model. There is an ever increasing demand to transmit larger number of superior-quality video channels into the available transmission bandwidth. Consumers are increasingly discerning about the quality and performance of video-based products and there is therefore a strong incentive for continuous improvement in video coding technology for companies to have market edge over its competitors. Even though processor speeds and network bandwidths continue to increase, a better video compression results in a more competitive product. This drive to improve video compression technology has led to a revolution in the last decade. In this thesis we addresses some of these data compression problems in a practical multimedia system that employ Hybrid video coding schemes. Typically Real life video signals show non-stationary statistical behavior. The statistics of these signals largely depend on the video content and the acquisition process. Hybrid video coding schemes like H264/AVC exploits some of the non-stationary characteristics but certainly not all of it. Moreover, higher order statistical dependencies on a syntax element level are mostly neglected in existing video coding schemes. Designing a video coding scheme for a video coder by taking into consideration these typically observed statistical properties, however, offers room for significant improvements in coding efficiency.In this thesis work a new frequency domain curve-fitting compression framework is proposed as an extension to H264 Context Adaptive Binary Arithmetic Coder (CABAC) that achieves better compression efficiency at reduced complexity. The proposed Curve-Fitting extension to H264 CABAC, henceforth called as CF-CABAC, is modularly designed to conveniently fit into existing block based H264 Hybrid video Entropy coding algorithms. Traditionally there have been many proposals in the literature to fuse surfaces/curve fitting with Block-based, Region based, Training-based (VQ, fractals) compression algorithms primarily to exploiting pixel- domain redundancies. Though the compression efficiency of these are expectantly better than DCT transform based compression, but their main drawback is the high computational demand which make the former techniques non-competitive for real-time applications over the latter. The curve fitting techniques proposed so far have been on the pixel domain. The video characteristic on the pixel domain are highly non-stationary making curve fitting techniques not very efficient in terms of video quality, compression ratio and complexity. In this thesis, we explore using curve fitting techniques to Quantized frequency domain coefficients. we fuse this powerful technique to H264 CABAC Entropy coding. Based on some predictable characteristics of Quantized DCT coefficients, a computationally in-expensive curve fitting technique is explored that fits into the existing H264 CABAC framework. Also Due to the lossy nature of video compression and the strong demand for bandwidth and computation resources in a multimedia system, one of the key design issues for video coding is to optimize trade-off among quality (distortion) vs compression (rate) vs complexity. This thesis also briefly studies the existing rate distortion (RD) optimization approaches proposed to video coding for exploring the best RD performance of a video codec. Further, we propose a graph based algorithm for Rate-distortion. optimization of quantized coefficient indices for the proposed CF-CABAC entropy coding.

In this thesis, a low bit rate video coding system based on three-dimensional (3-D) wavelet coding is studied. In addition to the initial motivation to make use of the motion compensated wavelet based coding schemes, the other techniques that do not utilize the motion compensation in their coding procedures have also been considered on equal footing. The 3-D wavelet transform (WT) algorithm is based on the &ldquo / group of frames&rdquo / (GOF) concept. The group of eight frames are decomposed both temporally and spatially to their coarse and detail parts. The decomposition process utilizes both orthogonal and bi-orthogonal wavelet analysis filters. The transform coefficients are coded using an embedded image coding algorithm, called the &ldquo / Two-Dimensional Set Partitioning in Hierarchical Trees&rdquo / (2-D SPIHT). Due to its nature, the 2-D SPIHT is applied to the individual subband frames. In the reconstruction phase, the 2-D SPIHT decoding algorithm and the wavelet synthesis filters are employed, respectively. The Peak Signal to Noise Ratios (PSNRs) are used as a quality measure of the reconstructed frames. The investigations reveal that among several factors, the multi-level (de)composition is the dominant one effective both on the signal compression and the PSNR level. The encoded videos compressed to the ratio of 1:9 are reconstructed with the PSNR of about 30 dB in the best cases.