Valid XHTML 1.0 Transitional

Valid CSS!

T-1: Distributed Video Coding for Low Cost Multimedia Communications and Systems

Monday Morning, June 23, 09:30 - 12:30

Presented by

W.A.C. Fernando, University of Surrey

Abstract

Video Coding technologies have evolved tremendously over past decades in line with the rapidly increasing demand over vastly expanding application domains. The research on video coding has been traditionally dominated by the work on MPEG and ITU-T H.26x standardisations based systems. It is known that in these mainstream technologies the video encoders are far more complex (by approximately 5 to 10 times) than the decoder structure. This architecture was motivated by many of the conventional one-to-many type video applications including broadcasting (DVB), video streaming etc where the decoder cost need to maintained considerably low for the benefit of large numbers of viewers compared to the limited number of content providers. However, more recently, this architecture is challenged by new consumer applications where the cost of encoder is a prime concern due to the necessity of vast deployments of video sensors. Security surveillance systems, mobile video conferencing, monitoring of the disabled people and children, disaster zone monitoring are a few potential scenarios which are largely benefited by massive encoder deployments. Distributed Video Coding (DVC) is an emerging video coding technology designed with a modified complexity balance between the encoder and decoder in line with the necessities of these applications. The dramatically low complexity of the DVC encoder helps these solutions by: (i) reducing the production cost of the signal processors of the video sensors, (ii) reducing the requirement of digital memory and (iii) reducing the power consumption which is generally a scarce resource at remote sites.

The research on DVC is still in preliminary stages and considerable amount of effort is necessary before going through the standardisation process and commercial use. The currently available literatures have used a number of hypothetical models and assumptions some of which have not yet been assessed for practical viability. Key frame transmission algorithm, noise distribution and error estimation at the decoder, implementation of the reverse feedback channel using the dynamic error estimation and the necessary communication protocols are some of the major open areas for research in DVC. Side information generation is an area largely discussed in literature, yet further room for development.

In DVC, the shift of complexity balance is achieved by moving the major task of the exploitation of source correlations to achieve the compression into the decoder. This task involves the generation of estimation, called side information, for the Wyner-Ziv frames of the video sequence. A sequence of ‘selected’ original frames is generally passed to the decoder using an intra frame coding scheme and are called key-frames. The frequency of key-frame transmission could vary on the DVC implementation strategy as determined by the Group of Picture (GOP) size. Different techniques have been proposed for the side information estimation ranging from simple frame interpolation/extrapolation through more complex and accurate motion estimation and compensation techniques. In DVC, the estimated side information is modelled with the original Wyner-Ziv frame and an additive white noise (AWN) term. The quantification of the statistical distribution properties of this hypothetical noise is a part of the ongoing research activity. The side information is used for decoding with the parity bit stream transmitted over the channel from the encoder. At the encoder this parity bits are generated by passing the original video sequence through a turbo encoder. This parity bit sequence is generally subjected to puncturing. Parity puncturing and the quantisation are the primary mechanisms for the video compression in DVC. Transform coding (e.g DCT) has been proposed for improving the compression efficiency of DVC, even though the DCT algorithm somewhat compromises the extremely low complexity in the pixel domain DVC encoder structure.

DVC makes use of, and operates within the theoretical framework and guidelines of distributed source coding set in the Slepian-Wolf theorem and the current DVC implementations are based in the Wyner-Ziv model. According to Wyner-Ziv model, the maximum compression efficiency is limited by the level of correlations between the side information (estimated) and the original Wyner-Ziv frame information and hence the main focus in DVC research is the design of accurate estimation algorithms.

In this tutorial we will discuss the motivations behind the DVC design, basic DVC codec architectures, current DVC codec architectures and possible modifications to enhance the performance, the hypothetical models and assumptions used in the current design together with design criteria for possible practical solutions and some of the potential application domains. Outline of the tutorial

  1. Brief overview to conventional video coding
  2. Overview of distributed source coding.
  3. Concept of DVC.
  4. The architecture of the Pixel Domain DVC codec based on turbo coding.
    1. Preparation of input bit stream using quantization & bitplane extraction for turbo encoding.
    2. Puncturing the parity bit stream for video compression.
    3. Side information generation using key-frames.
    4. Turbo decoding using side information and parity from encoder.
    5. Reconstruction function.
  5. Transform domain DVC codecs
  6. Modifications to the generic DVC codec architecture.
  7. The hypothetical assumptions and models used in the current architecture and designing practical solutions.
  8. Statistical modeling of noise distribution estimation at the decoder.
  9. Dynamic error estimation at the decoder.
  10. Unidirectional DVC systems.
  11. Multimedia Communications systems based on DVC.
    1. Encoder Design
    2. Decoder Design
    3. Complexity analysis
    4. Multimedia communications systems over noisy/wireless channels using DVC.
  12. The challenges in DVC realization and future research directions.
  13. Applications and business Models for DVC systems.
  14. Future of the distributed video coding and systems based on DVC.

Speaker Biography

Dr. W.A.C. Fernando (SMIEEE) leads the Video Codec group in University of Surrey, UK. He has been working in video coding since 1998 and has published more than 155 international refereed journal and proceeding papers in this area. Furthermore, he has published more than 28 international refereed journal and conference papers in DVC. He is also attached to the VISNET European project which covers lots of DVC activities as the leading institute. He has also lots of research collaborations in DVC within Europe and North America and Asia.

He is a member of the editorial board of the international journal of multimedia tools and applications. He has also been nominated as the guest editor for the special issue on joint source and channel coding for multimedia communications of the international journal of multimedia. Furthermore, he has been working as a referee for IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Communications, Mobile Computing, Communications Letters, IEE proceedings of communications, IEE proceedings of Vision and Computing, IEE Electronic Letters, Journal of Communications Networking, Electronics and Telecommunications Research journal, SPIE journals, etc., and many conference proceedings (VTC, ICC, ISCAS, ICIP, SPIE, ITC, etc.,).

Most Recent Tutorials: IEEE ICIP 2007, WPMC 2006, IWS/WPMC 2005, IEEE IV 2006 (London), IEEE IVS 2006 (Sydney)