Adaptive video delivery using semantics

The diffusion of network appliances such as cellular phones, personal digital assistants and hand-held computers has created the need to personalize the way media content is delivered to the end user. Moreover, recent devices, such as digital radio receivers with graphics displays, and new applications, such as intelligent visual surveillance, require novel forms of video analysis for content adaptation and summarization. To cope with these challenges, we propose an automatic method for the extraction of semantics from video, and we present a framework that exploits these semantics in order to provide adaptive video delivery. First, an algorithm that relies on motion information to extract multiple semantic video objects is proposed. The algorithm operates in two stages. In the first stage, a statistical change detector produces the segmentation of moving objects from the background. This process is robust with regard to camera noise and does not need manual tuning along a sequence or for different sequences. In the second stage, feedbacks between an object partition and a region partition are used to track individual objects along the frames. These interactions allow us to cope with multiple, deformable objects, occlusions, splitting, appearance and disappearance of objects, and complex motion. Subsequently, semantics are used to prioritize visual data in order to improve the performance of adaptive video delivery. The idea behind this approach is to organize the content so that a particular network or device does not inhibit the main content message. Specifically, we propose two new video adaptation strategies. The first strategy combines semantic analysis with a traditional frame-based video encoder. Background simplifications resulting from this approach do not penalize overall quality at low bitrates. The second strategy uses metadata to efficiently encode the main content message. The metadata-based representation of object's shape and motion suffices to convey the meaning and action of a scene when the objects are familiar. The impact of different video adaptation strategies is then quantified with subjective experiments. We ask a panel of human observers to rate the quality of adapted video sequences on a normalized scale. From these results, we further derive an objective quality metric, the semantic peak signal-to-noise ratio (SPSNR), that accounts for different image areas and for their relevance to the observer in order to reflect the focus of attention of the human visual system. At last, we determine the adaptation strategy that provides maximum value for the end user by maximizing the SPSNR for given client resources at the time of delivery. By combining semantic video analysis and adaptive delivery, the solution presented in this dissertation permits the distribution of video in complex media environments and supports a large variety of content-based applications

Space-variant picture coding

PhDSpace-variant picture coding techniques exploit the strong spatial non-uniformity of the human visual system in order to increase coding efficiency in terms of perceived quality per bit. This thesis extends space-variant coding research in two directions. The first of these directions is in foveated coding. Past foveated coding research has been dominated by the single-viewer, gaze-contingent scenario. However, for research into the multi-viewer and probability-based scenarios, this thesis presents a missing piece: an algorithm for computing an additive multi-viewer sensitivity function based on an established eye resolution model, and, from this, a blur map that is optimal in the sense of discarding frequencies in least-noticeable- rst order. Furthermore, for the application of a blur map, a novel algorithm is presented for the efficient computation of high-accuracy smoothly space-variant Gaussian blurring, using a specialised filter bank which approximates perfect space-variant Gaussian blurring to arbitrarily high accuracy and at greatly reduced cost compared to the brute force approach of employing a separate low-pass filter at each image location. The second direction is that of artifi cially increasing the depth-of- field of an image, an idea borrowed from photography with the advantage of allowing an image to be reduced in bitrate while retaining or increasing overall aesthetic quality. Two synthetic depth of field algorithms are presented herein, with the desirable properties of aiming to mimic occlusion eff ects as occur in natural blurring, and of handling any number of blurring and occlusion levels with the same level of computational complexity. The merits of this coding approach have been investigated by subjective experiments to compare it with single-viewer foveated image coding. The results found the depth-based preblurring to generally be significantly preferable to the same level of foveation blurring

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

Efficient DCT-Domain Blind Measurement of Blocking Artifacts

Abstract: It is well known that low bit rate block-based discrete cosine transform coded images exhibits visually annoying coding artifacts. It is of interest to be able to numerically assess the degree of blocking artifacts as it plays an important role in the design, optimization and assessment of image and video coding systems. A novel algorithm for image blocking artifact detection is presented in this paper. Experimental results illustrating the performance of proposed method are presented and evaluated. Our experiment results show that the proposed method of measuring blocking artifacts exhibits satisfactory performance as compared to other post-processing method's/techniques and is very efficient and stable since the signal need not be compressed/decompressed

High-fidelity imaging : the computational models of the human visual system in high dynamic range video compression, visible difference prediction and image processing

As new displays and cameras offer enhanced color capabilities, there is a need to extend the precision of digital content. High Dynamic Range (HDR) imaging encodes images and video with higher than normal bit-depth precision, enabling representation of the complete color gamut and the full visible range of luminance. This thesis addresses three problems of HDR imaging: the measurement of visible distortions in HDR images, lossy compression for HDR video, and artifact-free image processing. To measure distortions in HDR images, we develop a visual difference predictor for HDR images that is based on a computational model of the human visual system. To address the problem of HDR image encoding and compression, we derive a perceptually motivated color space for HDR pixels that can efficiently encode all perceivable colors and distinguishable shades of brightness. We use the derived color space to extend the MPEG-4 video compression standard for encoding HDR movie sequences. We also propose a backward-compatible HDR MPEG compression algorithm that encodes both a low-dynamic range and an HDR video sequence into a single MPEG stream. Finally, we propose a framework for image processing in the contrast domain. The framework transforms an image into multi-resolution physical contrast images (maps), which are then rescaled in just-noticeable-difference (JND) units. The application of the framework is demonstrated with a contrast-enhancing tone mapping and a color to gray conversion that preserves color saliency.Aktuelle Innovationen in der Farbverarbeitung bei Bildschirmen und Kameras erzwingen eine Präzisionserweiterung bei digitalen Medien. High Dynamic Range (HDR) kodieren Bilder und Video mit einer grösseren Bittiefe pro Pixel, und ermöglichen damit die Darstellung des kompletten Farbraums und aller sichtbaren Helligkeitswerte. Diese Arbeit konzentriert sich auf drei Probleme in der HDR-Verarbeitung: Messung von für den Menschen störenden Fehlern in HDR-Bildern, verlustbehaftete Kompression von HDR-Video, und visuell verlustfreie HDR-Bildverarbeitung. Die Messung von HDR-Bildfehlern geschieht mittels einer Vorhersage von sichtbaren Unterschieden zweier HDR-Bilder. Die Vorhersage basiert dabei auf einer Modellierung der menschlichen Sehens. Wir addressieren die Kompression und Kodierung von HDR-Bildern mit der Ableitung eines perzeptuellen Farbraums für HDR-Pixel, der alle wahrnehmbaren Farben und deren unterscheidbaren Helligkeitsnuancen effizient abbildet. Danach verwenden wir diesen Farbraum für die Erweiterung des MPEG-4 Videokompressionsstandards, welcher sich hinfort auch für die Kodierung von HDR-Videosequenzen eignet. Wir unterbreiten weiters eine rückwärts-kompatible MPEG-Kompression von HDR-Material, welche die übliche YUV-Bildsequenz zusammen mit dessen HDRVersion in einen gemeinsamen MPEG-Strom bettet. Abschliessend erklären wir unser Framework zur Bildverarbeitung in der Kontrastdomäne. Das Framework transformiert Bilder in mehrere physikalische Kontrastauflösungen, um sie danach in Einheiten von just-noticeable-difference (JND, noch erkennbarem Unterschied) zu reskalieren. Wir demonstrieren den Nutzen dieses Frameworks anhand von einem kontrastverstärkenden Tone Mapping-Verfahren und einer Graukonvertierung, die die urspr ünglichen Farbkontraste bestmöglich beibehält

A high-performance, low power and memory-efficient VLD for MPEG applications

An extremely important area that has enabled or will enable many of the digital video services and applications such as VideoCD, DVD, DVC, HDTV, video conferencing, and DSS is digital video compression. The great success of digital video compression is mainly because of two factors. The state of the art in very large scale integrated circuit (VLSI) and a considerable body of knowledge accumulated over the last several decades in applying video compression algorithms such as discrete cosine transform (DCT), motion estimation (ME), motion compensation (MC) and entropy coding techniques. The MPEG (Moving Pictures Expert Group) standard reflects the second factor. In this thesis, MPEG standards are discussed thoroughly and interpreted, and a VLSI chip implementation (CMOS 0.35μ technology and 3 layer metal) of a variable length decoder (VLD) for MPEG applications is developed. The VLD developed here achieves high performance by using a parallel and pipeline architecture. Furthermore, MPEG bitstream patterns are carefully analyzed in order to drastically improve VLD memory efficiency. Finally, a special clock scheme is applied to reduce the chip's power consumption

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Video Filtering Using Separable Four-Dimensional Nonlocal Spatiotemporal Transforms

The large number of practical application involving digital videos has motivated a significant interest in restoration or enhancement solutions to improve the visual quality under the presence of noise. We propose a powerful video denoising algorithm that exploits temporal and spatial redundancy characterizing natural video sequences to reduce the effects of noise. The algorithm implements the paradigm of nonlocal grouping and collaborative filtering, where a four-dimensional transform- domain representation is leveraged to enforce sparsity and thus regularize the data. Moreover we present an extension of our algorithm that can be effectively used as a deblocking and deringing filter to reduce the artifacts introduced by most of the popular video compression techniques. Our algorithm, termed V-BM4D, at first constructs three-dimensional volumes, by tracking blocks along trajectories defined by the motion vectors, and then groups together mutually similar volumes by stacking them along an additional fourth dimension. Each group is transformed through a decorrelating four-dimensional separable transform, and then it is collaboratively filtered by coeffcients shrinkage. The effectiveness of shrinkage is due to the sparse representation of the transformed group. Sparsity is achieved because of different type of correlation among the groups: local correlation along the two dimensions of the blocks, temporal correlation along the motion trajectories, and nonlocal spatial correlation along the fourth dimension. As a conclusive step, the different estimates of the filtered groups are adaptively aggregated and subsequently returned to their original position, to produce a final estimate of the original video. The proposed filtering procedure leads to excellent results in both objective and subjective visual quality, since in the restored video sequences the effect of the noise or of the compression artifacts is noticeably reduced, while the significant features are preserved. As demonstrated by experimental results, V-BM4D outperforms the state of the art in video denoising. /Kir1