David Bull - Intelligent Image and Video Compression: Communicating Pictures
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- Book:Intelligent Image and Video Compression: Communicating Pictures
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Intelligent Image and Video Compression: Communicating Pictures, Second Edition explains the requirements, analysis, design and application of a modern video coding system. It draws on the authors extensive academic and professional experience in this field to deliver a text that is algorithmically rigorous yet accessible, relevant to modern standards and practical. It builds on a thorough grounding in mathematical foundations and visual perception to demonstrate how modern image and video compression methods can be designed to meet the rate-quality performance levels demanded by todays applications and users, in the context of prevailing network constraints.
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Second edition
David R. Bull
Fan Zhang
- Tables in Chapter 1
- Tables in Chapter 4
- Tables in Chapter 6
- Tables in Chapter 7
- Tables in Chapter 8
- Tables in Chapter 9
- Tables in Chapter 10
- Tables in Chapter 11
- Tables in Chapter 12
- Tables in Chapter 13
- Figures
- Figures in Chapter 1
- Figures in Chapter 2
- Figures in Chapter 3
- Figures in Chapter 4
- Figures in Chapter 5
- Figures in Chapter 6
- Figures in Chapter 7
- Figures in Chapter 8
- Figures in Chapter 9
- Figures in Chapter 10
- Figures in Chapter 11
- Figures in Chapter 12
- Figures in Chapter 13
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ISBN: 978-0-12-820353-8
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- Figure 0.1 Drawing Ochre: c. 200,000 BCE, South Africa (Courtesy Professor Alice Roberts).
- Figure 1.1 A geometric interpretation of compression.
- Figure 1.2 The multimedia communications jigsaw puzzle.
- Figure 1.3 Simplified video compression architecture.
- Figure 1.4 The scope of standardization.
- Figure 1.5 A chronology of video coding standards from 1988 to the present date (dates in parentheses indicate first published standard and last published revision).
- Figure 1.6 The creative continuum.
- Figure 2.1 The visible spectrum.
- Figure 2.2 Cross-section of the human eye (public domain:
- Figure 2.3 Fundus image of a healthy retina (public domain:
- Figure 2.4 The focal length of the lens.
- Figure 2.5 Photoreceptor distribution in the retina (reproduced with permission from [13]).
- Figure 2.6 Normalized rod and cone responses for the human visual system (adapted from Bowmaker and Dartnall [14]; publicly available
- Figure 2.7 Retinal cell architecture (public domain image adapted from
- Figure 2.8 Spatial opponency, showing a center-surround cell and its firing pattern due to excitation.
- Figure 2.9 The visual cortex and visual pathways.
- Figure 2.10 Mach band effect.
- Figure 2.11 Adelson's grid (reproduced with permission from
- Figure 2.12 CIE luminous efficiency curve (publicly available:
- Figure 2.13 Dark adaptation of rods and cones.
- Figure 2.14 Increased immersion from color images.
- Figure 2.15 Opponent processing of color.
- Figure 2.16 The CIE.1931 chromaticity chart (reproduced with permission from [22]).
- Figure 2.17 Just noticeable differences at different contrast increments.
- Figure 2.18 JND curve for human vision.
- Figure 2.19 Contrast sensitivity chart.
- Figure 2.20 Luminance and chrominance CSF responses.
- Figure 2.21 Luminance contrast sensitivity function.
- Figure 2.22 Texture change blindness (images courtesy of Tom Troscianko).
- Figure 2.23 The importance of phase information in visual perception. Left: Original. Right: Phase-distorted version using the complex wavelet transform (reproduced from [16]).
- Figure 2.24 Perspective-based depth cues can be very compelling and misleading.
- Figure 2.25 Pits and bumps deceptive depth from lighting.
- Figure 2.26 The hollow mask illusion.
- Figure 2.27 Spatio-temporal CSF (adapted from Kelly [18]).
- Figure 2.28 Variation of critical flicker frequency (reproduced from Tyler [24]).
- Figure 2.29 Eye movements in response to a task (from Yarbus [29]; publicly available from
- Figure 2.30 Example of texture masking. (Top left) Three cues are shown on a gray background. (Top right) With zero-mean, 0.001 variance Gaussian noise. (Bottom left) With 0.01 variance noise. (Bottom right) With 0.03 variance noise.
- Figure 2.31 Edge masking for high and low dynamic range content.
- Figure 2.32 Temporal masking effects for various edge step sizes (adapted from Girod [31]).
- Figure 3.1 Spectral characteristics of sampling and aliasing.
- Figure 3.2 Demonstration of aliasing for a 1D signal: Top: Sinusoid sampled below Nyquist frequency. Bottom: Fourier plot showing spectral aliasing.
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