Video Compression Systems: From first principles to concatenated codecs
This book gives an overview on many practical aspects of video compression systems used in broadcast TV, IPTV, telecommunication and many other video applications. Although the book concentrates on MPEG realtime video compression systems, many aspects are equally applicable to offline and/or nonMPEG video compression applications.
Inspec keywords: motion estimation; video coding; mobile handsets; data compression; decoding; codecs; concatenated codes; statistical multiplexing; transcoding; high definition television
Other keywords: mobile device; picture quality assessment; bitstream processing; transcoding; concatenated codec; HDTV; digital video compression system; motion estimation; high definition television; statistical multiplexing; MPEG video compression; MPEG decoder
Subjects: High definition television; Communication switching; Video signal processing; Mobile radio systems; Image and video coding
 Book DOI: 10.1049/PBTE053E
 Chapter DOI: 10.1049/PBTE053E
 ISBN: 9780863419638
 eISBN: 9781849191036
 Page count: 304
 Format: PDF

Front Matter
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1 Introduction
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This chapter discusses video compression systems. The introduction of MPEG4 advanced video coding (AVC) compression into broadcast and telecommunication systems adds another level of complexity. Whereas just a few years ago, MPEG2 was the main video compression algorithm used throughout the broadcast industry, from contribution links right down to directtohome (DTH) applications, there are now different algorithms operating in separate parts of the production and transmission paths.

2 Digital video
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Digital video is becoming more and more popular. Not only are broadcasters changing over to digital transmission, but most video consumer products, such as camcorders, DVD recorders, etc., are now also using digital video signals.

3 Picture quality assessment
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Many chapters of this book make references to picture quality. Therefore, it is appropriate to give a brief overview of picture quality measurement methods. For example, statistical multiplexing systems, described in Chapter 12, have to have a measure of picture quality in order to allocate appropriate bit rates to each of the encoders. Although it will be shown in Chapter 4 that the quantisation parameter (QP) is one of the main factors affecting picture quality, there are many other factors influencing the quality of compressed video signals. This chapter gives a brief summary of picture quality assessment methods.

4 Compression principles
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Chapter 2 introduced some important aspects of analogue and digital video, particularly relevant to video compression. In this chapter we have a first look at some basic video compression techniques before we move on to specific MPEG algorithms. Although some of the examples are already based on MPEG2 coding tools, the principles explained in this chapter are applicable to the majority of video compression algorithms in use today. But before we explore the vast area of video compression methods, we will have a brief look at audio compression techniques.

5 MPEG video compression standards
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Having investigated the basic principles of video compression, it is time to have a look at some real compression algorithms. By far the most widely used family of video compression algorithms used in broadcast and telecommunication applications are the MPEG algorithms. The Moving Picture Experts Group (MPEG) is a working group of ISO/IEC (the International Organization for Standardisation/International Electrotechnical Commission), i.e. a non governmental international standards organisation. The first MPEG meeting was held in May 1988 in Ottawa, Canada. To date, MPEG has produced a number of highly successful international standards for video compression, as well as for multimedia content management.

6 NonMPEG compression algorithms
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Despite the fact that MPEG and the closely related ITU H.26x video compression standards are widely used in broadcast, telecommunications, IPTV as well as many other applications, there are, of course, numerous other video compression algorithms, each with its own advantages and niche applications. Although it goes beyond the scope of this book to introduce all commonly used compression algorithms, it is worth having a look at a few of the more important ones in order to show different approaches and algorithm variations. In particular, the Video Codec 1 (VC1) algorithm, developed by Microsoft, is a good example of a vendordriven, blockbased, motioncompensated compression algorithm with some interesting differences to MPEG algorithms. Since the decoding algorithm has been standardised in Society of Motion Picture and Tele vision Engineers (SMPTE), most of its compression tools are now in the public domain. Suitable for coding interlaced video signals and fully integrated with other Microsoft products, it provides an alternative for IPTV streaming applications. A second algorithm worth mentioning is the Chinese Audio Video Coding Standard (AVS). Although it has many similarities with MPEG4 (AVC), it avoids some of the most processingintensive parts of the MPEG4 (AVC) standard. In contrast to the blockbased VC1 and AVS algorithms, which have many similarities with MPEG algorithms, it is worth also looking at some nonblockbased compression algorithms, i.e. algorithms based on wavelet technology. Wavelet transforms are quite different from blockbased transforms. Therefore, a brief summary about wavelet theory is provided in Appendix E. Two algorithms need to be examined in more detail: JPEG 2000, which is becoming increasingly more relevant to broadcast applications, and Dirac, a family of opensource algorithms developed primarily by the BBC research department.

7 Motion estimation
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Motion estimation is arguably one the most important subfunctions of any motion compensated video compression algorithm  a reason enough to allocate a whole chapter to it. Although there are differences in terms of prediction modes and block sizes, the removal of temporal redundancy in video signals inevitably requires a search engine that provides motion information on predicted blocks or block par titions. However, motion estimation techniques are not only a major part of video compression algorithms, they are also used in noise reduction, deinterlacing (see also Chapter 8), standards conversion and many other videoprocessing applications. It is, therefore, not surprising that a large number of motion estimation algorithms have been developed for video compression as well as for other application areas.

8 Preprocessing
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Preprocessing is an important part of any professional compression encoder. It includes vital functions such as noise reduction, forward analysis, picture resizing and frame synchronisation. Very often, the preprocessing functions have direct connections with the main compression engine, which is why, in many cases, integrated preprocessing produces better results than external ones, even if external pieces of equipment might be more sophisticated. Preprocessing is as much a system function as it is an encoder function. It is closely related to most of the concepts discussed in remaining chapters of this book, and in many cases it would be beneficial to introduce the system aspects first before explaining how preprocessing functions can improve the endtoend performance. Nevertheless, it was felt that since preprocessing is an integral part of encoders, it should be explained in conjunction with compression algorithms rather than towards the end of the book. There are many crossreferences in this chapter, and the reader should not hesitate to read some of the other chapters before returning to this one.

9 High definition television (HDTV)
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After many years of research and several attempts to introduce HDTV into broadcast systems, HDTV has finally hit the consumer market. It took the combination of several factors to make it happen, the most important of these being the availability of large affordable display devices for consumers. A second important factor was the development of HDTVcapable successors to DVD players. Last but not least, the high compression efficiency of MPEG4 (AVC) makes it possible to transmit HDTV signals at bit rates that are not much higher than SDTV transmissions were in the early years of MPEG2. In fact, using DVBS2 forward error correction (FEC) and modulation, together with MPEG4 (AVC) compression, makes it possible to transmit HDTV sig nals within a bandwidth equivalent to that of SDTV MPEG2 compression with DVBS FEC and modulation.

10 Compression for mobile devices
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The requirements of video transmission systems to and from mobile devices differ considerably from those of satellite or terrestrial directtohome (DTH) transmissions. The main considerations are to keep the power consumption of mobile devices as low as possible and to make sure that frequency deviations due to the Doppler effect in moving receivers do not degrade the performance of the transmission system. In terms of video compression algorithms for mobile devices, there are also different requirements compared to traditional DTH applications. Not only should the compression algorithm provide high efficiency on relatively small, noninterlaced images, but it should also require little processing power and memory space for encoding as well as decoding.

11 MPEG decoders and postprocessing
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This chapter presents the channel change time between MPEG4 (AVC) bit streams tends to be generally longer than between MPEG2 bit streams due to larger buffer sizes and lower bit rates. Channel change time between different multiplexes is largely implementation dependent. Once a bit error has been detected by the decoder, the decoding process can only resume at the next slice header. In MPEG2 each row of macro blocks starts with a slice header, whereas in MPEG4 (AVC) slice headers are usually only at the start of a picture.

12 Statistical multiplexing
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CBR encoders have to be set to a bit rate close to the maximum bitrate demand in order to avoid compression artefacts. Most of the time the encoder can cope with a lower bit rate. By sharing a common bitrate capacity, encoders can free up bit rates to other channels during scenes of low criticality and obtain higher bit rates during critical scenes. By modelling the bitrate demand for different content, the bitrate saving of statistical multiplexing can be calculated.The bitrate demand of MPEG2 and MPEG4 (AVC) encoders is strongly correlated although the bitrate saving of MPEG4 (AVC) increases for less critical scenes. Noise reduction frees up bit rate for other channels in the statistical multiplexing group.

13 Compression for contribution and distribution
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In the previous chapter we had a look at statistical multiplexing systems that are mainly used for large headend transmission systems. However, MPEG compression is also used for pointtopoint transmissions between broadcast studios and for distribution to local studios or transmitters.

14 Concatenation and transcoding
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In the previous two chapters, we have seen that MPEG video compression is used for contribution and distribution (C&D) applications as well as for statistical multiplexing systems in DTH headend systems. Today, it is inevitable that video signals undergo several stages of compression and decompression before it reaches the end user, and with the growth of digital television networks, concatenation of compression encoding and decoding is becoming more and more prevalent. In this chapter we will have a closer look at all combinations of concatenation between MPEG2 and MPEG4 (AVC).

15 Bitstream processing
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This chapter presents the small bitrate changes can be achieved with bitrate changers, thus avoiding the need for decoders and encoders. Transcoders consist of closely coupled decoders and reencoders. Transcoders are used for large bitrate or profile changes or conversion from one compression standard to another. Splicing between statistically multiplexed video signals requires bitrate changers.

16 Concluding remarks
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Over the last 10 years, the compression efficiency of MPEG algorithms has improved significantly, and once all the compression tools of MPEG4 (AVC) have been fully exploited, it is difficult to see how it could be advanced even further. However, as processing power seems to increase steadily according to Moore's Law, more advanced algorithms can be conceived, and the question is not whether new compression algorithms will be developed, but rather at what point a new standard should be defined. Apart from MPEG, there are many research programmes and initiatives that could lead to future, more efficient, video compression standards. One of the areas in which compression efficiency could improve on MPEG4 (AVC) is how to deal with texture with random motion, for example splashing water. This type of content contains little redundancy and is difficult to compress. It has been shown that by synthesising such picture areas rather than trying to compress them, significant coding gains can be achieved.

Appendix A: Test sequences referred to in this book
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There are a number of test sequences referred to in this book. The tables in this appendix give a brief description of the sequences in terms of content and types of motion.

Appendix B: RGB/YUV conversion
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This appendix contains SDTV and HDTV conversion formulae between RGB and YUV and vice versa.

Appendix C: Definition of PSNR
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PSNR is calculated by summing up the squared pixel differences between the distorted and the source video signal. It has to be calculated for each component separately.

Appendix D: Discrete cosine transform (DCT) and inverse DCT
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The 8 × 8 twodimensional discrete cosine transform (DCT) and inverse DCT are defined.

Appendix E: Introduction to wavelet theory
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At first sight, wavelet transformation seems to combine several advantages of subband coding and conventional FFT or DCT while being computationally more efficient. The continuous nature of the transform, as opposed to DCT blocks, helps to avoid artefacts, and it appears to be better suited to the spatial decorrelation of texture in images. In the form of quadrature mirror filters (QMFs), a special case of wavelet filters has been known for some time. Wavelet theory generalises the principle of QMFs and provides a broader mathematical basis.

Appendix F: Comparison between phase correlation and crosscorrelation
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This appendix gives the formula for calculating the phase correlation surface calculated and by comparison, that for the crosscorrelation surface.

Appendix G: Polyphase filter design
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Design an FIR lowpass filter with a bandwidth of fsi/2N, where fsi is the input sampling frequency. The filter can be designed by inverse Fourier transforming the theoretical frequency response of the lowpass filter. Since the theoretical frequency response is a square wave, the inverse Fourier transform generates a sin(x)/x waveform of infinite length. After transformation into the spatial domain, a window function has to be applied to limit the number of filter coefficients.

Appendix H: Expected error propagation time
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Assuming that a bit error occurs somewhere in a group of pictures (GOP), the expected error propagation time through the GOP is calculated as a function of the GOP structure and the relative frame sizes of I, P and B frames.

Appendix I: Derivation of the bitrate demand model
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This appendix shows how the bitrate demand model is derived by defining the cumulative distribution function (CDF).

Back Matter
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