Statistical framework for video decoding complexity modeling and prediction

Video decoding complexity modeling and prediction is an increasingly important issue for efficient resource utilization in a variety of applications, including task scheduling, receiver-driven complexity shaping, and adaptive dynamic voltage scaling. In this paper we present a novel view of this problem based on a statistical framework perspective. We explore the statistical structure (clustering) of the execution time required by each video decoder module (entropy decoding, motion compensation, etc.) in conjunction with complexity features that are easily extractable at encoding time (representing the properties of each module's input source data). For this purpose, we employ Gaussian mixture models (GMMs) and an expectation-maximization algorithm to estimate the joint execution-time - feature probability density function (PDF). A training set of typical video sequences is used for this purpose in an offline estimation process. The obtained GMM representation is used in conjunction with the complexity features of new video sequences to predict the execution time required for the decoding of these sequences. Several prediction approaches are discussed and compared. The potential mismatch between the training set and new video content is addressed by adaptive online joint-PDF re-estimation. An experimental comparison is performed to evaluate the different approaches and compare the proposed prediction scheme with related resource prediction schemes from the literature. The usefulness of the proposed complexity-prediction approaches is demonstrated in an application of rate-distortion-complexity optimized decoding

Ubiquitous Scalable Graphics: An End-to-End Framework using Wavelets

Advances in ubiquitous displays and wireless communications have fueled the emergence of exciting mobile graphics applications including 3D virtual product catalogs, 3D maps, security monitoring systems and mobile games. Current trends that use cameras to capture geometry, material reflectance and other graphics elements means that very high resolution inputs is accessible to render extremely photorealistic scenes. However, captured graphics content can be many gigabytes in size, and must be simplified before they can be used on small mobile devices, which have limited resources, such as memory, screen size and battery energy. Scaling and converting graphics content to a suitable rendering format involves running several software tools, and selecting the best resolution for target mobile device is often done by trial and error, which all takes time. Wireless errors can also affect transmitted content and aggressive compression is needed for low-bandwidth wireless networks. Most rendering algorithms are currently optimized for visual realism and speed, but are not resource or energy efficient on mobile device. This dissertation focuses on the improvement of rendering performance by reducing the impacts of these problems with UbiWave, an end-to-end Framework to enable real time mobile access to high resolution graphics using wavelets. The framework tackles the issues including simplification, transmission, and resource efficient rendering of graphics content on mobile device based on wavelets by utilizing 1) a Perceptual Error Metric (PoI) for automatically computing the best resolution of graphics content for a given mobile display to eliminate guesswork and save resources, 2) Unequal Error Protection (UEP) to improve the resilience to wireless errors, 3) an Energy-efficient Adaptive Real-time Rendering (EARR) heuristic to balance energy consumption, rendering speed and image quality and 4) an Energy-efficient Streaming Technique. The results facilitate a new class of mobile graphics application which can gracefully adapt the lowest acceptable rendering resolution to the wireless network conditions and the availability of resources and battery energy on mobile device adaptively

Scalable and network aware video coding for advanced communications over heterogeneous networks

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThis work addresses the issues concerned with the provision of scalable video services over heterogeneous networks particularly with regards to dynamic adaptation and user’s acceptable quality of service. In order to provide and sustain an adaptive and network friendly multimedia communication service, a suite of techniques that achieved automatic scalability and adaptation are developed. These techniques are evaluated objectively and subjectively to assess the Quality of Service (QoS) provided to diverse users with variable constraints and dynamic resources. The research ensured the consideration of various levels of user acceptable QoS The techniques are further evaluated with view to establish their performance against state of the art scalable and non-scalable techniques. To further improve the adaptability of the designed techniques, several experiments and real time simulations are conducted with the aim of determining the optimum performance with various coding parameters and scenarios. The coding parameters and scenarios are evaluated and analyzed to determine their performance using various types of video content and formats. Several algorithms are developed to provide a dynamic adaptation of coding tools and parameters to specific video content type, format and bandwidth of transmission. Due to the nature of heterogeneous networks where channel conditions, terminals, users capabilities and preferences etc are unpredictably changing, hence limiting the adaptability of a specific technique adopted, a Dynamic Scalability Decision Making Algorithm (SADMA) is developed. The algorithm autonomously selects one of the designed scalability techniques basing its decision on the monitored and reported channel conditions. Experiments were conducted using a purpose-built heterogeneous network simulator and the network-aware selection of the scalability techniques is based on real time simulation results. A technique with a minimum delay, low bit-rate, low frame rate and low quality is adopted as a reactive measure to a predicted bad channel condition. If the use of the techniques is not favoured due to deteriorating channel conditions reported, a reduced layered stream or base layer is used. If the network status does not allow the use of the base layer, then the stream uses parameter identifiers with high efficiency to improve the scalability and adaptation of the video service. To further improve the flexibility and efficiency of the algorithm, a dynamic de-blocking filter and lambda value selection are analyzed and introduced in the algorithm. Various methods, interfaces and algorithms are defined for transcoding from one technique to another and extracting sub-streams when the network conditions do not allow for the transmission of the entire bit-stream

Optimal layered representation for adaptive interactive multiview video streaming

We consider an interactive multiview video streaming (IMVS) system where clients select their preferred viewpoint in a given navigation window. To provide high quality IMVS, many high quality views should be transmitted to the clients. However, this is not always possible due to the limited and heterogeneous capabilities of the clients. In this paper, we propose a novel adaptive IMVS solution based on a layered multiview representation where camera views are organized into layered subsets to match the different clients constraints. We formulate an optimization problem for the joint selection of the views subsets and their encoding rates. Then, we propose an optimal and a reduced computational complexity greedy algorithms, both based on dynamic-programming. Simulation results show the good performance of our novel algorithms compared to a baseline algorithm, proving that an effective IMVS adaptive solution should consider the scene content and the client capabilities and their preferences in navigation

Error resilient H.264 coded video transmission over wireless channels

The H.264/AVC recommendation was first published in 2003 and builds on the concepts of earlier standards such as MPEG-2 and MPEG-4. The H.264 recommendation represents an evolution of the existing video coding standards and was developed in response to the growing need for higher compression. Even though H.264 provides for greater compression, H.264 compressed video streams are very prone to channel errors in mobile wireless fading channels such as 3G due to high error rates experienced. Common video compression techniques include motion compensation, prediction methods, transformation, quantization and entropy coding, which are the common elements of a hybrid video codecs. The ITU-T recommendation H.264 introduces several new error resilience tools, as well as several new features such as Intra Prediction and Deblocking Filter. The channel model used for the testing was the Rayleigh Fading channel with the noise component simulated as Additive White Gaussian Noise (AWGN) using QPSK as the modulation technique. The channel was used over several Eb/N0 values to provide similar bit error rates as those found in the literature. Though further research needs to be conducted, results have shown that when using the H.264 error resilience tools in protecting encoded bitstreams to minor channel errors improvement in the decoded video quality can be observed. The tools did not perform as well with mild and severe channel errors significant as the resultant bitstream was too corrupted. From this, further research in channel coding techniques is needed to determine if the bitstream can be protected from these sorts of error rate

Robust and efficient video/image transmission

The Internet has become a primary medium for information transmission. The unreliability of channel conditions, limited channel bandwidth and explosive growth of information transmission requests, however, hinder its further development. Hence, research on robust and efficient delivery of video/image content is demanding nowadays. Three aspects of this task, error burst correction, efficient rate allocation and random error protection are investigated in this dissertation. A novel technique, called successive packing, is proposed for combating multi-dimensional (M-D) bursts of errors. A new concept of basis interleaving array is introduced. By combining different basis arrays, effective M-D interleaving can be realized. It has been shown that this algorithm can be implemented only once and yet optimal for a set of error bursts having different sizes for a given two-dimensional (2-D) array. To adapt to variable channel conditions, a novel rate allocation technique is proposed for FineGranular Scalability (FGS) coded video, in which real data based rate-distortion modeling is developed, constant quality constraint is adopted and sliding window approach is proposed to adapt to the variable channel conditions. By using the proposed technique, constant quality is realized among frames by solving a set of linear functions. Thus, significant computational simplification is achieved compared with the state-of-the-art techniques. The reduction of the overall distortion is obtained at the same time. To combat the random error during the transmission, an unequal error protection (UEP) method and a robust error-concealment strategy are proposed for scalable coded video bitstreams

Description-driven Adaptation of Media Resources

The current multimedia landscape is characterized by a significant diversity in terms of available media formats, network technologies, and device properties. This heterogeneity has resulted in a number of new challenges, such as providing universal access to multimedia content. A solution for this diversity is the use of scalable bit streams, as well as the deployment of a complementary system that is capable of adapting scalable bit streams to the constraints imposed by a particular usage environment (e.g., the limited screen resolution of a mobile device). This dissertation investigates the use of an XML-driven (Extensible Markup Language) framework for the format-independent adaptation of scalable bit streams. Using this approach, the structure of a bit stream is first translated into an XML description. In a next step, the resulting XML description is transformed to reflect a desired adaptation of the bit stream. Finally, the transformed XML description is used to create an adapted bit stream that is suited for playback in the targeted usage environment. The main contribution of this dissertation is BFlavor, a new tool for exposing the syntax of binary media resources as an XML description. Its development was inspired by two other technologies, i.e. MPEG-21 BSDL (Bitstream Syntax Description Language) and XFlavor (Formal Language for Audio-Visual Object Representation, extended with XML features). Although created from a different point of view, both languages offer solutions for translating the syntax of a media resource into an XML representation for further processing. BFlavor (BSDL+XFlavor) harmonizes the two technologies by combining their strengths and eliminating their weaknesses. The expressive power and performance of a BFlavor-based content adaptation chain, compared to tool chains entirely based on either BSDL or XFlavor, were investigated by several experiments. One series of experiments targeted the exploitation of multi-layered temporal scalability in H.264/AVC, paying particular attention to the use of sub-sequences and hierarchical coding patterns, as well as to the use of metadata messages to communicate the bit stream structure to the adaptation logic. BFlavor was the only tool to offer an elegant and practical solution for XML-driven adaptation of H.264/AVC bit streams in the temporal domain