Highly efficient low-level feature extraction for video representation and retrieval.

PhDWitnessing the omnipresence of digital video media, the research community has raised the question of its meaningful use and management. Stored in immense multimedia databases, digital videos need to be retrieved and structured in an intelligent way, relying on the content and the rich semantics involved. Current Content Based Video Indexing and Retrieval systems face the problem of the semantic gap between the simplicity of the available visual features and the richness of user semantics. This work focuses on the issues of efficiency and scalability in video indexing and retrieval to facilitate a video representation model capable of semantic annotation. A highly efficient algorithm for temporal analysis and key-frame extraction is developed. It is based on the prediction information extracted directly from the compressed domain features and the robust scalable analysis in the temporal domain. Furthermore, a hierarchical quantisation of the colour features in the descriptor space is presented. Derived from the extracted set of low-level features, a video representation model that enables semantic annotation and contextual genre classification is designed. Results demonstrate the efficiency and robustness of the temporal analysis algorithm that runs in real time maintaining the high precision and recall of the detection task. Adaptive key-frame extraction and summarisation achieve a good overview of the visual content, while the colour quantisation algorithm efficiently creates hierarchical set of descriptors. Finally, the video representation model, supported by the genre classification algorithm, achieves excellent results in an automatic annotation system by linking the video clips with a limited lexicon of related keywords

Semi-automatic video object segmentation for multimedia applications

A semi-automatic video object segmentation tool is presented for segmenting both still pictures and image sequences. The approach comprises both automatic segmentation algorithms and manual user interaction. The still image segmentation component is comprised of a conventional spatial segmentation algorithm (Recursive Shortest Spanning Tree (RSST)), a hierarchical segmentation representation method (Binary Partition Tree (BPT)), and user interaction. An initial segmentation partition of homogeneous regions is created using RSST. The BPT technique is then used to merge these regions and hierarchically represent the segmentation in a binary tree. The semantic objects are then manually built by selectively clicking on image regions. A video object-tracking component enables image sequence segmentation, and this subsystem is based on motion estimation, spatial segmentation, object projection, region classification, and user interaction. The motion between the previous frame and the current frame is estimated, and the previous object is then projected onto the current partition. A region classification technique is used to determine which regions in the current partition belong to the projected object. User interaction is allowed for object re-initialisation when the segmentation results become inaccurate. The combination of all these components enables offline video sequence segmentation. The results presented on standard test sequences illustrate the potential use of this system for object-based coding and representation of multimedia

Error control strategies in H.265|HEVC video transmission

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWith the rapid development in video coding technologies in the last decade, high-resolution video delivery suffers from packet loss due to unreliable transmission channels (time-varying characteristics). The error Resilience approaches at channel coding level are less efficient to implement in real time video transmission as the encoded video samples are in variable code length. Therefore, error resilience in video coding standard plays a vital role to reduce the effect of error propagation and improve the perceived visual quality. The main work in this thesis is to develop an efficient error resilience mechanism for H.265|HEVC video coding standard to reduce the effects of error propagation in error-prone conditions. In this thesis, two error resilience algorithms are proposed. The first one is Adaptive Slice Encoding (ASE) error resilience algorithm. The concept of this algorithm is to extract and protect the most active slices in the coded bitstream based on the adaptive search window. This algorithm can be applied in low delay video transmission with and without using a feedback channel. It is also designed to be compatible with reference coding software manual (HM16) for H.265|HEVC coding standard. The second proposed algorithm is a joint encoder-decoder error resilience called Error resilience based on Supplemental Enhancement Information (ERSEI) algorithm. A feedback message status is used from the decoder to notify the encoder to start encoding clean random-access picture adaptively based on the decoded picture hash message status from the decoder. At the same time, the decoder will be notified to start the error concealment process whilst waiting to receive correct video data. A recovery point message from the decoder feedback channel is used to update the encoder with error messages. In this thesis, extensive experimental work, evaluation, and comparison with state-of-the-art related algorithms have been conducted to evaluate the proposed algorithms. Furthermore, the best trade-off between the coding efficiency of the proposed error resilience algorithms and error resilience performance has been considered at the design stage. The experimental work evaluation includes both encoding conditions, i.e. error-free and error-prone. The results achieved from the experiments show significant improvements, in (Y-PSNR) results and subjective quality of the decoded bitstream, using the proposed algorithm in error-prone conditions with a variety of packet loss rates. Moreover, experimental work is conducted to test the algorithms complexity in terms of required processing execution time at both encoding and decoding stages. Additionally, the video coding standard performance for both H.264|AVC and H.265|HEVC coding standards are evaluated in error-free and error-prone environments. For ASE algorithm and when compared with improved region of interest (IROI) and region of interest (ROI) algorithms, a significant improvement in visual quality was the most obvious finding from the obtained results with PLRs of 2-18 (%). For ERSEI algorithm and when compared with the default HM16 with pixel copy concealment and motion compensated error concealment (MCEC) techniques, the evaluation results indicate clear visual quality enhancement under different packet loss rates PLRs (1,2 6, 8) %.The Ministry of Higher Education and Scientific Research in Ira

Task complexity analysis and QoS management for mapping dynamic video-processing tasks on a multi-core platform

This paper addresses efficient mapping and reconfiguration of advanced video applications onto a general purpose multi-core platform. By accurately modeling the resource usage for an application, allocation of processing resources on the platform can be based on the actually needed resources instead of a worst-case approach, thereby improving Quality-of-Service (QoS). Here, we exploit a new and strongly upcoming class of dynamic video applications based on image and content analysis for resource management and control. Such applications are characterized by irregular computing behavior and memory usage. It is shown that with linear models and statistical techniques based on the Markov modeling, a rather good accuracy (94–97%) for predicting the resource usage can be obtained. This prediction accuracy is so good that it allows resource prediction at runtime, thereby leading to an actively controlled system management

Energy efficient enabling technologies for semantic video processing on mobile devices

Semantic object-based processing will play an increasingly important role in future multimedia systems due to the ubiquity of digital multimedia capture/playback technologies and increasing storage capacity. Although the object based paradigm has many undeniable benefits, numerous technical challenges remain before the applications becomes pervasive, particularly on computational constrained mobile devices. A fundamental issue is the ill-posed problem of semantic object segmentation. Furthermore, on battery powered mobile computing devices, the additional algorithmic complexity of semantic object based processing compared to conventional video processing is highly undesirable both from a real-time operation and battery life perspective. This thesis attempts to tackle these issues by firstly constraining the solution space and focusing on the human face as a primary semantic concept of use to users of mobile devices. A novel face detection algorithm is proposed, which from the outset was designed to be amenable to be offloaded from the host microprocessor to dedicated hardware, thereby providing real-time performance and reducing power consumption. The algorithm uses an Artificial Neural Network (ANN), whose topology and weights are evolved via a genetic algorithm (GA). The computational burden of the ANN evaluation is offloaded to a dedicated hardware accelerator, which is capable of processing any evolved network topology. Efficient arithmetic circuitry, which leverages modified Booth recoding, column compressors and carry save adders, is adopted throughout the design. To tackle the increased computational costs associated with object tracking or object based shape encoding, a novel energy efficient binary motion estimation architecture is proposed. Energy is reduced in the proposed motion estimation architecture by minimising the redundant operations inherent in the binary data. Both architectures are shown to compare favourable with the relevant prior art

Analysis of affine motion-compensated prediction and its application in aerial video coding

Motion-compensated prediction is used in video coding standards like High Efficiency Video Coding (HEVC) as one key element of data compression. Commonly, a purely translational motion model is employed. In order to also cover non-translational motion types like rotation or scaling (zoom) contained in aerial video sequences such as captured from unmanned aerial vehicles, an affine motion model can be applied. In this work, a model for affine motion-compensated prediction in video coding is derived by extending a model of purely translational motion-compensated prediction. Using the rate-distortion theory and the displacement estimation error caused by inaccurate affine motion parameter estimation, the minimum required bit rate for encoding the prediction error is determined. In this model, the affine transformation parameters are assumed to be affected by statistically independent estimation errors, which all follow a zero-mean Gaussian distributed probability density function (pdf). The joint pdf of the estimation errors is derived and transformed into the pdf of the location-dependent displacement estimation error in the image. The latter is related to the minimum required bit rate for encoding the prediction error. Similar to the derivations of the fully affine motion model, a four-parameter simplified affine model is investigated. It is of particular interest since such a model is considered for the upcoming video coding standard Versatile Video Coding (VVC) succeeding HEVC. As the simplified affine motion model is able to describe most motions contained in aerial surveillance videos, its application in video coding is justified. Both models provide valuable information about the minimum bit rate for encoding the prediction error as a function of affine estimation accuracies. Although the bit rate in motion-compensated prediction can be considerably reduced by using a motion model which is able to describe motion types occurring in the scene, the total video bit rate may remain quite high, depending on the motion estimation accuracy. Thus, at the example of aerial surveillance sequences, a codec independent region of interest- ( ROI -) based aerial video coding system is proposed that exploits the characteristic of such sequences. Assuming the captured scene to be planar, one frame can be projected into another using global motion compensation. Consequently, only new emerging areas have to be encoded. At the decoder, all new areas are registered into a so-called mosaic. From this, reconstructed frames are extracted and concatenated as a video sequence. To also preserve moving objects in the reconstructed video, local motion is detected and encoded in addition to the new areas. The proposed general ROI coding system was evaluated for very low and low bit rates between 100 and 5000 kbit/s for aerial sequences of HD resolution. It is able to reduce the bit rate by 90% compared to common HEVC coding of similar quality. Subjective tests confirm that the overall image quality of the ROI coding system exceeds that of a common HEVC encoder especially at very low bit rates below 1 Mbit/s. To prevent discontinuities introduced by inaccurate global motion estimation, as may be caused by radial lens distortion, a fully automatic in-loop radial distortion compensation is proposed. For this purpose, an unknown radial distortion compensation parameter that is constant for a group of frames is jointly estimated with the global motion. This parameter is optimized to minimize the distortions of the projections of frames in the mosaic. By this approach, the global motion compensation was improved by 0.27dB and discontinuities in the frames extracted from the mosaic are diminished. As an additional benefit, the generation of long-term mosaics becomes possible, constructed by more than 1500 aerial frames with unknown radial lens distortion and without any calibration or manual lens distortion compensation.Bewegungskompensierte Prädiktion wird in Videocodierstandards wie High Efficiency Video Coding (HEVC) als ein Schlüsselelement zur Datenkompression verwendet. Typischerweise kommt dabei ein rein translatorisches Bewegungsmodell zum Einsatz. Um auch nicht-translatorische Bewegungen wie Rotation oder Skalierung (Zoom) beschreiben zu können, welche beispielsweise in von unbemannten Luftfahrzeugen aufgezeichneten Luftbildvideosequenzen enthalten sind, kann ein affines Bewegungsmodell verwendet werden. In dieser Arbeit wird aufbauend auf einem rein translatorischen Bewegungsmodell ein Modell für affine bewegungskompensierte Prädiktion hergeleitet. Unter Verwendung der Raten-Verzerrungs-Theorie und des Verschiebungsschätzfehlers, welcher aus einer inexakten affinen Bewegungsschätzung resultiert, wird die minimal erforderliche Bitrate zur Codierung des Prädiktionsfehlers hergeleitet. Für die Modellierung wird angenommen, dass die sechs Parameter einer affinen Transformation durch statistisch unabhängige Schätzfehler gestört sind. Für jeden dieser Schätzfehler wird angenommen, dass die Wahrscheinlichkeitsdichteverteilung einer mittelwertfreien Gaußverteilung entspricht. Aus der Verbundwahrscheinlichkeitsdichte der Schätzfehler wird die Wahrscheinlichkeitsdichte des ortsabhängigen Verschiebungsschätzfehlers im Bild berechnet. Letztere wird schließlich zu der minimalen Bitrate in Beziehung gesetzt, welche für die Codierung des Prädiktionsfehlers benötigt wird. Analog zur obigen Ableitung des Modells für das voll-affine Bewegungsmodell wird ein vereinfachtes affines Bewegungsmodell mit vier Freiheitsgraden untersucht. Ein solches Modell wird derzeit auch im Rahmen der Standardisierung des HEVC-Nachfolgestandards Versatile Video Coding (VVC) evaluiert. Da das vereinfachte Modell bereits die meisten in Luftbildvideosequenzen vorkommenden Bewegungen abbilden kann, ist der Einsatz des vereinfachten affinen Modells in der Videocodierung gerechtfertigt. Beide Modelle liefern wertvolle Informationen über die minimal benötigte Bitrate zur Codierung des Prädiktionsfehlers in Abhängigkeit von der affinen Schätzgenauigkeit. Zwar kann die Bitrate mittels bewegungskompensierter Prädiktion durch Wahl eines geeigneten Bewegungsmodells und akkurater affiner Bewegungsschätzung stark reduziert werden, die verbleibende Gesamtbitrate kann allerdings dennoch relativ hoch sein. Deshalb wird am Beispiel von Luftbildvideosequenzen ein Regionen-von-Interesse- (ROI-) basiertes Codiersystem vorgeschlagen, welches spezielle Eigenschaften solcher Sequenzen ausnutzt. Unter der Annahme, dass eine aufgenommene Szene planar ist, kann ein Bild durch globale Bewegungskompensation in ein anderes projiziert werden. Deshalb müssen vom aktuellen Bild prinzipiell nur noch neu im Bild erscheinende Bereiche codiert werden. Am Decoder werden alle neuen Bildbereiche in einem gemeinsamen Mosaikbild registriert, aus dem schließlich die Einzelbilder der Videosequenz rekonstruiert werden können. Um auch lokale Bewegungen abzubilden, werden bewegte Objekte detektiert und zusätzlich zu neuen Bildbereichen als ROI codiert. Die Leistungsfähigkeit des ROI-Codiersystems wurde insbesondere für sehr niedrige und niedrige Bitraten von 100 bis 5000 kbit/s für Bilder in HD-Auflösung evaluiert. Im Vergleich zu einer gewöhnlichen HEVC-Codierung kann die Bitrate um 90% reduziert werden. Durch subjektive Tests wurde bestätigt, dass das ROI-Codiersystem insbesondere für sehr niedrige Bitraten von unter 1 Mbit/s deutlich leistungsfähiger in Bezug auf Detailauflösung und Gesamteindruck ist als ein herkömmliches HEVC-Referenzsystem. Um Diskontinuitäten in den rekonstruierten Videobildern zu vermeiden, die durch eine durch Linsenverzeichnungen induzierte ungenaue globale Bewegungsschätzung entstehen können, wird eine automatische Radialverzeichnungskorrektur vorgeschlagen. Dabei wird ein unbekannter, jedoch über mehrere Bilder konstanter Korrekturparameter gemeinsam mit der globalen Bewegung geschätzt. Dieser Parameter wird derart optimiert, dass die Projektionen der Bilder in das Mosaik möglichst wenig verzerrt werden. Daraus resultiert eine um 0,27dB verbesserte globale Bewegungskompensation, wodurch weniger Diskontinuitäten in den aus dem Mosaik rekonstruierten Bildern entstehen. Dieses Verfahren ermöglicht zusätzlich die Erstellung von Langzeitmosaiken aus über 1500 Luftbildern mit unbekannter Radialverzeichnung und ohne manuelle Korrektur

3D multiple description coding for error resilience over wireless networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Mobile communications has gained a growing interest from both customers and service providers alike in the last 1-2 decades. Visual information is used in many application domains such as remote health care, video –on demand, broadcasting, video surveillance etc. In order to enhance the visual effects of digital video content, the depth perception needs to be provided with the actual visual content. 3D video has earned a significant interest from the research community in recent years, due to the tremendous impact it leaves on viewers and its enhancement of the user’s quality of experience (QoE). In the near future, 3D video is likely to be used in most video applications, as it offers a greater sense of immersion and perceptual experience. When 3D video is compressed and transmitted over error prone channels, the associated packet loss leads to visual quality degradation. When a picture is lost or corrupted so severely that the concealment result is not acceptable, the receiver typically pauses video playback and waits for the next INTRA picture to resume decoding. Error propagation caused by employing predictive coding may degrade the video quality severely. There are several ways used to mitigate the effects of such transmission errors. One widely used technique in International Video Coding Standards is error resilience. The motivation behind this research work is that, existing schemes for 2D colour video compression such as MPEG, JPEG and H.263 cannot be applied to 3D video content. 3D video signals contain depth as well as colour information and are bandwidth demanding, as they require the transmission of multiple high-bandwidth 3D video streams. On the other hand, the capacity of wireless channels is limited and wireless links are prone to various types of errors caused by noise, interference, fading, handoff, error burst and network congestion. Given the maximum bit rate budget to represent the 3D scene, optimal bit-rate allocation between texture and depth information rendering distortion/losses should be minimised. To mitigate the effect of these errors on the perceptual 3D video quality, error resilience video coding needs to be investigated further to offer better quality of experience (QoE) to end users. This research work aims at enhancing the error resilience capability of compressed 3D video, when transmitted over mobile channels, using Multiple Description Coding (MDC) in order to improve better user’s quality of experience (QoE). Furthermore, this thesis examines the sensitivity of the human visual system (HVS) when employed to view 3D video scenes. The approach used in this study is to use subjective testing in order to rate people’s perception of 3D video under error free and error prone conditions through the use of a carefully designed bespoke questionnaire.Petroleum Technology Development Fund (PTDF