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

Analysis of Affine Motion-Compensated Prediction in 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), e. g. contained in aerial video sequences such as captured from unmanned aerial vehicles (UAV), an affine motion model can be applied. In this work, a model for affine motion-compensated prediction in video coding is derived. 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 pdfof 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. Both models are of particular interest since they are considered for the upcoming video coding standard Versatile Video Coding (VVC) succeeding HEVC. Both models provide valuable information about the minimum bit rate for encoding the prediction error as a function of affine estimation accuracies. © 1992-2012 IEEE

Towards Hybrid-Optimization Video Coding

Video coding is a mathematical optimization problem of rate and distortion essentially. To solve this complex optimization problem, two popular video coding frameworks have been developed: block-based hybrid video coding and end-to-end learned video coding. If we rethink video coding from the perspective of optimization, we find that the existing two frameworks represent two directions of optimization solutions. Block-based hybrid coding represents the discrete optimization solution because those irrelevant coding modes are discrete in mathematics. It searches for the best one among multiple starting points (i.e. modes). However, the search is not efficient enough. On the other hand, end-to-end learned coding represents the continuous optimization solution because the gradient descent is based on a continuous function. It optimizes a group of model parameters efficiently by the numerical algorithm. However, limited by only one starting point, it is easy to fall into the local optimum. To better solve the optimization problem, we propose to regard video coding as a hybrid of the discrete and continuous optimization problem, and use both search and numerical algorithm to solve it. Our idea is to provide multiple discrete starting points in the global space and optimize the local optimum around each point by numerical algorithm efficiently. Finally, we search for the global optimum among those local optimums. Guided by the hybrid optimization idea, we design a hybrid optimization video coding framework, which is built on continuous deep networks entirely and also contains some discrete modes. We conduct a comprehensive set of experiments. Compared to the continuous optimization framework, our method outperforms pure learned video coding methods. Meanwhile, compared to the discrete optimization framework, our method achieves comparable performance to HEVC reference software HM16.10 in PSNR

Segmentation-based video coding system allowing the manipulation of objects

This paper presents a generic video coding algorithm allowing the content-based manipulation of objects. This manipulation is possible thanks to the definition of a spatiotemporal segmentation of the sequences. The coding strategy relies on a joint optimization in the rate-distortion sense of the partition definition and of the coding techniques to be used within each region. This optimization creates the link between the analysis and synthesis parts of the coder. The analysis defines the time evolution of the partition, as well as the elimination or the appearance of regions that are homogeneous either spatially or in motion. The coding of the texture as well as of the partition relies on region-based motion compensation techniques. The algorithm offers a good compromise between the ability to track and manipulate objects and the coding efficiency.Peer ReviewedPostprint (published version

Centralized and distributed semi-parametric compression of piecewise smooth functions

This thesis introduces novel wavelet-based semi-parametric centralized and distributed compression methods for a class of piecewise smooth functions. Our proposed compression schemes are based on a non-conventional transform coding structure with simple independent encoders and a complex joint decoder. Current centralized state-of-the-art compression schemes are based on the conventional structure where an encoder is relatively complex and nonlinear. In addition, the setting usually allows the encoder to observe the entire source. Recently, there has been an increasing need for compression schemes where the encoder is lower in complexity and, instead, the decoder has to handle more computationally intensive tasks. Furthermore, the setup may involve multiple encoders, where each one can only partially observe the source. Such scenario is often referred to as distributed source coding. In the first part, we focus on the dual situation of the centralized compression where the encoder is linear and the decoder is nonlinear. Our analysis is centered around a class of 1-D piecewise smooth functions. We show that, by incorporating parametric estimation into the decoding procedure, it is possible to achieve the same distortion- rate performance as that of a conventional wavelet-based compression scheme. We also present a new constructive approach to parametric estimation based on the sampling results of signals with finite rate of innovation. The second part of the thesis focuses on the distributed compression scenario, where each independent encoder partially observes the 1-D piecewise smooth function. We propose a new wavelet-based distributed compression scheme that uses parametric estimation to perform joint decoding. Our distortion-rate analysis shows that it is possible for the proposed scheme to achieve that same compression performance as that of a joint encoding scheme. Lastly, we apply the proposed theoretical framework in the context of distributed image and video compression. We start by considering a simplified model of the video signal and show that we can achieve distortion-rate performance close to that of a joint encoding scheme. We then present practical compression schemes for real world signals. Our simulations confirm the improvement in performance over classical schemes, both in terms of the PSNR and the visual quality

Region-based representations of image and video: segmentation tools for multimedia services

This paper discusses region-based representations of image and video that are useful for multimedia services such as those supported by the MPEG-4 and MPEG-7 standards. Classical tools related to the generation of the region-based representations are discussed. After a description of the main processing steps and the corresponding choices in terms of feature spaces, decision spaces, and decision algorithms, the state of the art in segmentation is reviewed. Mainly tools useful in the context of the MPEG-4 and MPEG-7 standards are discussed. The review is structured around the strategies used by the algorithms (transition based or homogeneity based) and the decision spaces (spatial, spatio-temporal, and temporal). The second part of this paper proposes a partition tree representation of images and introduces a processing strategy that involves a similarity estimation step followed by a partition creation step. This strategy tries to find a compromise between what can be done in a systematic and universal way and what has to be application dependent. It is shown in particular how a single partition tree created with an extremely simple similarity feature can support a large number of segmentation applications: spatial segmentation, motion estimation, region-based coding, semantic object extraction, and region-based retrieval.Peer ReviewedPostprint (published version

A comprehensive video codec comparison

In this paper, we compare the video codecs AV1 (version 1.0.0-2242 from August 2019), HEVC (HM and x265), AVC (x264), the exploration software JEM which is based on HEVC, and the VVC (successor of HEVC) test model VTM (version 4.0 from February 2019) under two fair and balanced configurations: All Intra for the assessment of intra coding and Maximum Coding Efficiency with all codecs being tuned for their best coding efficiency settings. VTM achieves the highest coding efficiency in both configurations, followed by JEM and AV1. The worst coding efficiency is achieved by x264 and x265, even in the placebo preset for highest coding efficiency. AV1 gained a lot in terms of coding efficiency compared to previous versions and now outperforms HM by 24% BD-Rate gains. VTM gains 5% over AV1 in terms of BD-Rates. By reporting separate numbers for JVET and AOM test sequences, it is ensured that no bias in the test sequences exists. When comparing only intra coding tools, it is observed that the complexity increases exponentially for linearly increasing coding efficiency

Object-based video representations: shape compression and object segmentation

Object-based video representations are considered to be useful for easing the process of multimedia content production and enhancing user interactivity in multimedia productions. Object-based video presents several new technical challenges, however. Firstly, as with conventional video representations, compression of the video data is a requirement. For object-based representations, it is necessary to compress the shape of each video object as it moves in time. This amounts to the compression of moving binary images. This is achieved by the use of a technique called context-based arithmetic encoding. The technique is utilised by applying it to rectangular pixel blocks and as such it is consistent with the standard tools of video compression. The blockbased application also facilitates well the exploitation of temporal redundancy in the sequence of binary shapes. For the first time, context-based arithmetic encoding is used in conjunction with motion compensation to provide inter-frame compression. The method, described in this thesis, has been thoroughly tested throughout the MPEG-4 core experiment process and due to favourable results, it has been adopted as part of the MPEG-4 video standard. The second challenge lies in the acquisition of the video objects. Under normal conditions, a video sequence is captured as a sequence of frames and there is no inherent information about what objects are in the sequence, not to mention information relating to the shape of each object. Some means for segmenting semantic objects from general video sequences is required. For this purpose, several image analysis tools may be of help and in particular, it is believed that video object tracking algorithms will be important. A new tracking algorithm is developed based on piecewise polynomial motion representations and statistical estimation tools, e.g. the expectationmaximisation method and the minimum description length principle

Towards visualization and searching :a dual-purpose video coding approach

In modern video applications, the role of the decoded video is much more than filling a screen for visualization. To offer powerful video-enabled applications, it is increasingly critical not only to visualize the decoded video but also to provide efficient searching capabilities for similar content. Video surveillance and personal communication applications are critical examples of these dual visualization and searching requirements. However, current video coding solutions are strongly biased towards the visualization needs. In this context, the goal of this work is to propose a dual-purpose video coding solution targeting both visualization and searching needs by adopting a hybrid coding framework where the usual pixel-based coding approach is combined with a novel feature-based coding approach. In this novel dual-purpose video coding solution, some frames are coded using a set of keypoint matches, which not only allow decoding for visualization, but also provide the decoder valuable feature-related information, extracted at the encoder from the original frames, instrumental for efficient searching. The proposed solution is based on a flexible joint Lagrangian optimization framework where pixel-based and feature-based processing are combined to find the most appropriate trade-off between the visualization and searching performances. Extensive experimental results for the assessment of the proposed dual-purpose video coding solution under meaningful test conditions are presented. The results show the flexibility of the proposed coding solution to achieve different optimization trade-offs, notably competitive performance regarding the state-of-the-art HEVC standard both in terms of visualization and searching performance.Em modernas aplicações de vídeo, o papel do vídeo decodificado é muito mais que simplesmente preencher uma tela para visualização. Para oferecer aplicações mais poderosas por meio de sinais de vídeo,é cada vez mais crítico não apenas considerar a qualidade do conteúdo objetivando sua visualização, mas também possibilitar meios de realizar busca por conteúdos semelhantes. Requisitos de visualização e de busca são considerados, por exemplo, em modernas aplicações de vídeo vigilância e comunicações pessoais. No entanto, as atuais soluções de codificação de vídeo são fortemente voltadas aos requisitos de visualização. Nesse contexto, o objetivo deste trabalho é propor uma solução de codificação de vídeo de propósito duplo, objetivando tanto requisitos de visualização quanto de busca. Para isso, é proposto um arcabouço de codificação em que a abordagem usual de codificação de pixels é combinada com uma nova abordagem de codificação baseada em features visuais. Nessa solução, alguns quadros são codificados usando um conjunto de pares de keypoints casados, possibilitando não apenas visualização, mas também provendo ao decodificador valiosas informações de features visuais, extraídas no codificador a partir do conteúdo original, que são instrumentais em aplicações de busca. A solução proposta emprega um esquema flexível de otimização Lagrangiana onde o processamento baseado em pixel é combinado com o processamento baseado em features visuais objetivando encontrar um compromisso adequado entre os desempenhos de visualização e de busca. Os resultados experimentais mostram a flexibilidade da solução proposta em alcançar diferentes compromissos de otimização, nomeadamente desempenho competitivo em relação ao padrão HEVC tanto em termos de visualização quanto de busca

Self-similarity and wavelet forms for the compression of still image and video data

This thesis is concerned with the methods used to reduce the data volume required to represent still images and video sequences. The number of disparate still image and video coding methods increases almost daily. Recently, two new strategies have emerged and have stimulated widespread research. These are the fractal method and the wavelet transform. In this thesis, it will be argued that the two methods share a common principle: that of self-similarity. The two will be related concretely via an image coding algorithm which combines the two, normally disparate, strategies. The wavelet transform is an orientation selective transform. It will be shown that the selectivity of the conventional transform is not sufficient to allow exploitation of self-similarity while keeping computational cost low. To address this, a new wavelet transform is presented which allows for greater orientation selectivity, while maintaining the orthogonality and data volume of the conventional wavelet transform. Many designs for vector quantizers have been published recently and another is added to the gamut by this work. The tree structured vector quantizer presented here is on-line and self structuring, requiring no distinct training phase. Combining these into a still image data compression system produces results which are among the best that have been published to date. An extension of the two dimensional wavelet transform to encompass the time dimension is straightforward and this work attempts to extrapolate some of its properties into three dimensions. The vector quantizer is then applied to three dimensional image data to produce a video coding system which, while not optimal, produces very encouraging results