Communicating Pictures (eBook)

A Course in Image and Video Coding

David Bull (Autor)

eBook Download: PDF | EPUB

2014 | 1. Auflage
560 Seiten
Elsevier Science (Verlag)
978-0-08-099374-4 (ISBN)

Communicating Pictures starts with a unique historical perspective of the role of images in communications and then builds on this to explain the applications and requirements of a modern video coding system. It draws on the author's extensive academic and professional experience of signal processing and video coding to deliver a text that is algorithmically rigorous, yet accessible, relevant to modern standards, and practical. It offers a thorough grounding in visual perception, and demonstrates how modern image and video compression methods can be designed in order to meet the rate-quality performance levels demanded by today's applications, networks and users. With this book you will learn:

Practical issues when implementing a codec, such as picture boundary extension and complexity reduction, with particular emphasis on efficient algorithms for transforms, motion estimators and error resilience
Conflicts between conventional video compression, based on variable length coding and spatiotemporal prediction, and the requirements for error resilient transmission
How to assess the quality of coded images and video content, both through subjective trials and by using perceptually optimised objective metrics
Features, operation and performance of the state-of-the-art High Efficiency Video Coding (HEVC) standard

Covers the basics of video communications and includes a strong grounding in how we perceive images and video, and how we can exploit redundancy to reduce bitrate and improve rate distortion performance
Gives deep insight into the pitfalls associated with the transmission of real-time video over networks (wireless and fixed)
Uses the state-of- the-art video coding standard (H.264/AVC) as a basis for algorithm development in the context of block based compression
Insight into future video coding standards such as the new ISO/ITU High Efficiency Video Coding (HEVC) initiative, which extends and generalizes the H.264/AVC approach

David Bull is the Chair in Signal Processing and Head of the Visual Information Laboratory at the University of Bristol, and Director of the Bristol Vision Institute. His research interests are focused on image and video communications and analysis for wireless, internet and broadcast applications. He has published over 450 academic papers, various articles, three books and numerous patents, many of which have been exploited commercially. He has undertaken a wide range of consultation activities for both industries and governments around the world.

Communicating Pictures starts with a unique historical perspective of the role of images in communications and then builds on this to explain the applications and requirements of a modern video coding system. It draws on the author's extensive academic and professional experience of signal processing and video coding to deliver a text that is algorithmically rigorous, yet accessible, relevant to modern standards, and practical. It offers a thorough grounding in visual perception, and demonstrates how modern image and video compression methods can be designed in order to meet the rate-quality performance levels demanded by today's applications, networks and users.With this book you will learn: Practical issues when implementing a codec, such as picture boundary extension and complexity reduction, with particular emphasis on efficient algorithms for transforms, motion estimators and error resilience Conflicts between conventional video compression, based on variable length coding and spatiotemporal prediction, and the requirements for error resilient transmission How to assess the quality of coded images and video content, both through subjective trials and by using perceptually optimised objective metrics Features, operation and performance of the state-of-the-art High Efficiency Video Coding (HEVC) standard Covers the basics of video communications and includes a strong grounding in how we perceive images and video, and how we can exploit redundancy to reduce bitrate and improve rate distortion performance Gives deep insight into the pitfalls associated with the transmission of real-time video over networks (wireless and fixed) Uses the state-of- the-art video coding standard (H.264/AVC) as a basis for algorithm development in the context of block based compression Insight into future video coding standards such as the new ISO/ITU High Efficiency Video Coding (HEVC) initiative, which extends and generalizes the H.264/AVC approach

List of figures

Fig. 1.1

A geometric interpretation of compression.

Fig. 1.2

The multimedia communications jigsaw puzzle.

Fig. 1.3

Simplified high level video compression architecture.

Fig. 1.4

The scope of standardization.

Fig. 1.5

A chronology of video coding standards from 1990 to the present date.

Fig. 2.1

The visible spectrum.

Fig. 2.2

Cross-section of the human eye. (Public domain: http://commons.wikimedia.org/wiki/File:Schematic_diagram_of_the_human_eye_en.svg.)

Fig. 2.3

Fundus image of a healthy retina. (Public domain from: http://commons.wikimedia.org/wiki/File:Fundus_photograph_of_normal_right_eye.jpg.)

Fig. 2.4

The focal length of the lens.

Fig. 2.5

Photoreceptor distribution in the retina. (Reproduced with permission from: Mustafia et al. [12].)

Fig. 2.6

Normalized rod and cone responses for the human visual system. (Reproduced with permission from: Bowmaker and Dartnall [33]. //www.ncbi.nlm.nih.gov/pmc/articles/PMC1279132/. Avail Wikimedia Commons.)

Fig. 2.7

Retinal cell architecture (Public domain image adapted from http://commons.wikimedia.org/wiki/File:Retina-diagram.svg).

Fig. 2.8

Spatial opponency, showing center-surround on cell and its firing pattern due to excitation.

Fig. 2.9

The visual cortex. (Reproduced from: http://www.expertsmind.com/topic/neuroscience/eye-and-visual-pathways-93024.aspx.)

Fig. 2.10

Mach band effect.

Fig. 2.11

Adelson’s grid. (Reproduced with permission from: http://web.mit.edu/persci/people/adelson/checkershadow_illusion.html.)

Fig. 2.12

CIE luminous efficiency curve. (Public domain image: http://en.wikipedia.org/wiki/File:CIE_1931_Luminosity.png.)

Fig. 2.13

Dark adaptation of rods and cones.

Fig. 2.14

Increased immersion from color images.

Fig. 2.15

Opponent processing of color.

Fig. 2.16

Color dependence on context. The bottom picture is just a squashed version of the top one yet the green stripes become blue.

Fig. 2.17

The CIE 1931 chromaticity chart. (Reproduced with permission from Ref. [21].)

Fig. 2.18

Just-noticeable differences at different contrast increments.

Fig. 2.19

JND curve for human vision.

Fig. 2.20

Contrast sensitivity chart.

Fig. 2.21

Luminance and chrominance CSF responses.

Fig. 2.22

Luminance contrast sensitivity function.

Fig. 2.23

Texture change blindness. (Images courtesy of Tom Troscianko.)

Fig. 2.24

The importance of phase information in visual perception. Left: original. Right: phase distorted version using the complex wavelet transform (Reproduced with permission from Vilankar et al. [14]).

Fig. 2.25

Perspective-based depth cues can be very compelling and misleading.

Fig. 2.26

Pits and bumps—deceptive depth from lighting.

Fig. 2.27

The hollow mask illusion.

Fig. 2.28

Spatio-temporal CSF. (Adapted from Kelly [18].)

Fig. 2.29

Variation of critical flicker frequency (Reproduced with permission from Tyler [23]).

Fig. 2.30

Eye movements in response to task (Public domain image from: http://commons.wikimedia.org/wiki/File:Yarbus_The_Visitor.jpg).

Fig. 2.31

Example of texture masking.

Fig. 2.32

Edge masking for high and low dynamic range content.

Fig. 2.33

Temporal masking effects for various edge step sizes. (Reproduced with permission from Girod [30].)

Fig. 3.1

Spectral characteristics of sampling and aliasing.

Fig. 3.2

Demonstration of aliasing for a 1-D signal. Top: sinusoid sampled below Nyquist frequency. Bottom: Fourier plot showing spectral aliasing.

Fig. 3.3

2-D spectral characteristics of sampling and aliasing. Left: Top—original signal spectrum; Bottom—sampled signal spectrum with no aliasing. Right: Top—original signal spectrum; Bottom—sampled signal spectrum with aliasing due to sub-Nyquist sampling.

Fig. 3.4

Hexagonal sampling lattice and its reciprocal as defined by equation (3.8).

Fig. 3.5

Example image histogram for 256 × 256 image Stampe_SV4.

Fig. 3.6

Autocorrelation plots for Acer image (512 × 512). Top left to bottom right: original image; autocorrelation function for row 100; autocorrelation function for whole image; 2-D autocorrelation surface.

Fig. 3.7

Autocorrelation plots for Stampe_SV4 image (512 × 512). Top left to bottom right: original image; autocorrelation function for row 100; autocorrelation function for whole image; 2-D autocorrelation surface.

Fig. 3.8

Autocorrelation plots for Stampe_SV4 image (256 × 256). Top left to bottom right: original image; autocorrelation function for row 100; autocorrelation function for whole image; 2-D autocorrelation surface.

Fig. 3.9

Temporal autocorrelation plots for Foreman (30 fps). Top to bottom right: sample frame showing selected 16 × 16 block used; temporal correlation for a single pixel; temporal correlation for a 16 × 16 block.

Fig. 3.10

Filterbank responses for the LeGall low-pass and high-pass analysis filters.

Fig. 3.11

Filter response for H.264 half-pixel interpolation filter.

Fig. 3.12

Common uniform quantizer characteristics.

Fig. 3.13

Common non-uniform quantizers. Left: center deadzone. Right: Lloyd Max quantizer.

Fig. 3.14

Feedforward linear prediction. Top: encoder. Bottom: decoder.

Fig. 3.15

Prediction signal dynamic range. Top: input signal. Bottom left: distribution of 1000 samples of input signal. Bottom right: distribution of 1000 samples of prediction residual.

Fig. 3.16

Feedback-based linear prediction.

Fig. 3.17

Feedback-based linear predictor with quantization noise modeling.

Fig. 3.18

Self-information and probability. Left: plot of self-information vs probability for a single event. Right: plot of the self-information of an event weighted by its probability.

Fig. 4.1

Image sample array.

100

Fig. 4.2

Image samples.

101

Fig. 4.3

Pixelation at varying resolutions. Top left to bottom right: 256 × 256; 64 × 64; 32 × 32; 16 × 16.

102

Fig. 4.4

Typical macroblock structure.

103

Fig. 4.5

Typical group of pictures structure and prediction modes.

105

Fig. 4.6

Aspect ratios of common formats, normalized according to resolution.

107

Fig. 4.7

Widescreen formats.

108

Fig. 4.8

Variation of field of view with viewing distance (aspect ratio = 16:9 here).

109

Fig. 4.9

Interlaced vs progressive frame scanning.

111

Fig. 4.10

Example of effects of interlaced scanning with poor...

Erscheint lt. Verlag	19.7.2014
Sprache	englisch
Themenwelt	Mathematik / Informatik ► Informatik ► Grafik / Design
	Technik ► Elektrotechnik / Energietechnik
	Technik ► Nachrichtentechnik
ISBN-10	0-08-099374-5 / 0080993745
ISBN-13	978-0-08-099374-4 / 9780080993744

Haben Sie eine Frage zum Produkt?

PDF (Adobe DRM)
Größe: 30,2 MB

Kopierschutz: Adobe-DRM
Adobe-DRM ist ein Kopierschutz, der das eBook vor Mißbrauch schützen soll. Dabei wird das eBook bereits beim Download auf Ihre persönliche Adobe-ID autorisiert. Lesen können Sie das eBook dann nur auf den Geräten, welche ebenfalls auf Ihre Adobe-ID registriert sind.
Details zum Adobe-DRM

Dateiformat: PDF (Portable Document Format)
Mit einem festen Seitenlayout eignet sich die PDF besonders für Fachbücher mit Spalten, Tabellen und Abbildungen. Eine PDF kann auf fast allen Geräten angezeigt werden, ist aber für kleine Displays (Smartphone, eReader) nur eingeschränkt geeignet.

Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen eine Adobe-ID und die Software Adobe Digital Editions (kostenlos). Von der Benutzung der OverDrive Media Console raten wir Ihnen ab. Erfahrungsgemäß treten hier gehäuft Probleme mit dem Adobe DRM auf.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen eine Adobe-ID sowie eine kostenlose App.
Geräteliste und zusätzliche Hinweise

Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.

EPUB (Adobe DRM)
Größe: 26,4 MB

Dateiformat: EPUB (Electronic Publication)
EPUB ist ein offener Standard für eBooks und eignet sich besonders zur Darstellung von Belletristik und Sachbüchern. Der Fließtext wird dynamisch an die Display- und Schriftgröße angepasst. Auch für mobile Lesegeräte ist EPUB daher gut geeignet.

Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.

Print-Ausgabe

Buch | Hardcover

78,55 €