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Rate-Quality Optimized Video Coding (Paperback, Softcover reprint of the original 1st ed. 1999)
Loot Price: R2,631
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Rate-Quality Optimized Video Coding (Paperback, Softcover reprint of the original 1st ed. 1999)
Series: The Springer International Series in Engineering and Computer Science, 486
Expected to ship within 18 - 22 working days
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Rate-Quality Optimized Video Coding discusses the matter of
optimizing (or negotiating) the data rate of compressed digital
video and its quality, which has been a relatively neglected topic
in either side of image/video coding and tele-traffic management.
Video rate management becomes a technically challenging task since
it is required to maintain a certain video quality regardless of
the availability of transmission or storage media. This is caused
by the broadband nature of digital video and inherent algorithmic
features of mainstream video compression schemes, e.g. H.261, H.263
and MPEG series. In order to maximize the media utilization and to
enhance video quality, the data rate of compressed video should be
regulated within a budget of available media resources while
maintaining the video quality as high as possible. In Part I
(Chapters 1 to 4) the non-stationarity of digital video is
discussed. Since the non-stationary nature is also inherited from
algorithmic properties of international video coding standards,
which are a combination of statistical coding techniques, the video
rate management techniques of these standards are explored.
Although there is a series of known video rate control techniques,
such as picture rate variation, frame dropping, etc., these
techniques do not view the matter as an optimization between rate
and quality. From the view of rate-quality optimization, the
quantizer is the sole means of controling rate and quality. Thus,
quantizers and quantizer control techniques are analyzed, based on
the relationship of rate and quality. In Part II (Chapters 5 and
6), as a coherent approach to non-stationary video, established but
still thriving nonlinear techniques are applied to video
rate-quality optimization such as artificial neural networks
including radical basis function networks, and fuzzy logic-based
schemes. Conventional linear techniques are also described before
the nonlinear techniques are explored. By using these nonlinear
techniques, it is shown how they influence and tackle the
rate-quality optimization problem. Finally, in Chapter 7
rate-quality optimization issues are reviewed in emerging video
communication applications such as video transcoding and mobile
video. This chapter discusses some new issues and prospects of rate
and quality control in those technology areas. Rate-Quality
Optimized Video Coding is an excellent reference and can be used
for advanced courses on the topic.
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