数字视频处理技术与高清接口

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"数字视频处理方法，主要涉及高清电视（HDTV）的相关算法和技术接口" 在数字视频处理领域，特别是与高清电视（HDTV）相关的技术，涉及到一系列复杂的算法和接口设计。数字视频处理是将模拟视频信号转换为数字形式，并进行一系列操作，如编码、压缩、解码和显示等，以实现高质量的视频播放和传输。首先，高清电视（HDTV）是指具有比传统电视更高分辨率的电视系统，通常提供1920x1080像素或1280x720像素的分辨率，提供更清晰、更细腻的图像质量。HDTV技术的发展推动了数字视频处理技术的进步，对图像压缩算法、视频编码标准（如MPEG系列、H.264/AVC、HEVC等）以及实时视频处理硬件的需求增加。在《Digital Video and HDTV Algorithms and Interfaces》一书中，作者Charles Poynton深入探讨了这些关键主题。其中，视频编码算法旨在有效地减少视频数据量，以便在有限的带宽下传输，同时保持视觉质量。这包括块运动估计、熵编码和预测编码等技术。接口设计则是连接不同设备和系统的关键，如摄像机、编码器、解码器和显示器之间的通信协议和数据格式。此外，书中的内容可能还涵盖了颜色空间转换、数字信号处理、视频质量评估、错误隐藏和恢复策略，以及与视频流传输相关的网络协议（如RTP/RTCP、HTTP Live Streaming等）。在HDTV中，对色彩管理和动态范围的精确控制也至关重要，因为它们直接影响到观众的观看体验。数字视频处理还涉及到视频增强技术，如去噪、锐化、降噪、超分辨率和帧率转换，这些都是为了提升视频的视觉效果。在实时处理和交互性方面，低延迟编码和解码技术也是研究的重点。此外，随着计算机图形学和几何建模的发展，数字视频处理与3D图形、纹理建模、级别细节（LOD）技术、曲线和曲面建模、非真实感渲染（NPR）等领域的结合日益紧密。这些技术在虚拟现实、游戏开发、影视制作等领域有着广泛的应用。数字视频处理方法不仅包含HDTV的技术基础，还包括了一系列与之相关的图像处理、编码解码、网络传输和用户界面设计等多方面的知识，这些都是构建现代视频系统不可或缺的组成部分。通过理解并掌握这些技术，可以实现更高效、更高质量的视频服务。

10 DIGITAL VIDEO AND HDTV ALGORITHMS AND INTERFACES

Spatiotemporal domains

A sequence of still pictures captured and displayed at

a sufficiently high rate

–

typically between 24 and 60

pictures per second

–

can create the illusion of motion,

as I will describe on page 51. Sampling in time, in

combination with 2-D (spatial) sampling, causes digital

video to be sampled in three axes

–

horizontal, vertical,

and temporal

–

as sketched in Figure 1.13 above. One-

dimensional sampling theory, to be detailed in Filtering

and sampling, on page 141, applies along each axis.

At the left of Figure 1.13 is a sketch of a two-dimen-

sional spatial domain of a single image. Some image

processing operations, such as certain kinds of filtering,

can be performed separately on the horizontal and

vertical axes, and have an effect in the spatial domain –

these operations are called separable. Other processing

operations cannot be separated into horizontal and

vertical facets, and must be performed directly on

a two-dimensional sample array. Two-dimensional

sampling will be detailed in Image digitization and

reconstruction, on page 187.

VERTICAL

TEMPORAL

SPATIAL

HORIZONTAL

(TRANSVERSE)

Figure 1.13 Spatio-

temporal domains

CHAPTER 1 RASTER IMAGES 11

Lightness terminology

In a grayscale image, each pixel value represents what is

loosely called brightness. However, brightness is defined

formally as the attribute of a visual sensation according

to which an area appears to emit more or less light. This

definition is obviously subjective, so brightness is an

inappropriate metric for image data.

See Appendix B, Introduction to

radiometry and photometry, on

page 601.

Intensity is radiant power in a particular direction;

radiance is intensity per unit projected area. These

terms disregard wavelength composition. But in color

imaging, wavelength is important! Neither of these

quantities is a suitable metric for color image data.

Luminance is radiance weighted by the spectral sensi-

tivity associated with the brightness sensation of vision.

Luminance is proportional to intensity. Imaging systems

rarely use pixel values proportional to luminance; values

nonlinearly related to luminance are usually used.

Illuminance is luminance integrated over a half-sphere.

Lightness

–

formally, CIE L*

–

is the standard approxi-

mation to the perceptual response to luminance. It is

computed by subjecting luminance to a nonlinear

transfer function that mimics vision. A few grayscale

imaging systems have pixel values proportional to L*.

Regrettably, many practitioners of

computer graphics, and of digital

image processing, have a cavalier

attitude toward these terms. In the

HSB, HSI, HSL, and HSV systems,

B allegedly stands for brightness,

I for intensity, L for lightness, and

V for value. None of these systems

computes brightness, intensity,

luminance, or value according to

any definition that is recognized in

color science!

Value refers to measures of lightness apart from CIE L*.

In image science, value is rarely

–

if ever

–

used in any

sense consistent with accurate color. (Several different

value scales are graphed in Figure 20.2 on page 208.)

Color images are sensed and reproduced based upon

tristimulus values, whose amplitudes are proportional to

intensity but whose spectral compositions are carefully

chosen according to the principles of color science. As

their name implies, tristimulus values come in sets of 3.

The image sensor of a digital camera produces values,

proportional to radiance, that approximate red, green,

and blue (RGB) tristimulus values. (I call these values

linear-light.) However, in most imaging systems, RGB

tristimulus values are subject to a nonlinear transfer

The term luminance is often care-

lessly and incorrectly used to refer

to luma; see below. In image

reproduction, we are usually

concerned not with (absolute)

luminance, but with relative lumi-

nance, to be detailed on page 206.

12 DIGITAL VIDEO AND HDTV ALGORITHMS AND INTERFACES

function

–

gamma correction

–

that mimics the percep-

tual response. Most imaging systems use RGB values

that are not proportional to intensity. The notation

R’G’B’ denotes the nonlinearity.

See Appendix A, YUV and luminance

considered harmful, on page 595.

Luma (Y’) is formed as a suitably weighted sum of

R’G’B’; it is the basis of luma/color difference coding.

Luma is comparable to lightness; it is often carelessly

and incorrectly called luminance by video engineers.

Nonlinear image coding

Vision cannot distinguish two luminance levels if the

ratio between them is less than about 1.01

–

in other

words, the visual threshold for luminance difference is

about 1%. This contrast sensitivity threshold is estab-

lished by experiments using the test pattern such as the

one sketched in Figure 1.14 in the margin; details will

be presented in Contrast sensitivity, on page 198.

Consider pixel values proportional to luminance, where

code zero represents black, and the maximum code

value of 255 represents white, as in Figure 1.15.

Code 100 lies at the point on the scale where the ratio

between adjacent luminance values is 1%: The

boundary between a region of code 100 samples and

a region of code 101 samples is likely to be visible.

As the pixel value decreases below 100, the difference

in luminance between adjacent codes becomes increas-

ingly perceptible: At code 25, the ratio between adja-

cent luminance values is 4%. In a large area of smoothly

varying shades of gray, these luminance differences are

likely to be visible or even objectionable. Visible jumps

in luminance produce artifacts known as contouring or

banding.

Linear-light codes above 100 suffer no banding arti-

facts. However, as code value increases toward white,

the codes have decreasing perceptual utility: At code

200, the luminance ratio between adjacent codes is just

0.5%, near the threshold of visibility. Codes 200 and

201 are visually indistinguishable; code 201 could be

discarded without its absence being noticed.

Y+∆Y

Figure 1.14 Contrast sensi-

tivity test pattern reveals

that a just-noticeable differ-

ence (JND) occurs when the

step between luminance

levels is 1% of Y.

∆ = 1%

100

101

255

∆ = 0.5%

200

201

∆ = 4%

2.55 : 1

Figure 1.15 The “code 100”

problem with linear-light

coding is that at code levels

below 100, the steps between

code values have ratios larger

than the visual threshold: The

steps are liable to be visible.

CHAPTER 1 RASTER IMAGES 13

High-quality image reproduction requires a ratio of at

least 30 to 1 between the luminance of white and the

luminance of black, as I will explain in Contrast ratio, on

page 197. In 8-bit linear-light coding, the ratio between

the brightest luminance (code 255) and the darkest

luminance that can be reproduced without banding

(code 100) is only 2.55:1. Linear-light coding in 8 bits is

unsuitable for high-quality images.

This “code 100” problem can be mitigated by placing

the top end of the scale at a code value higher than

100, as sketched in Figure 1.16 in the margin. If lumi-

nance is represented in 12 bits, white is at code 4095;

the luminance ratio between code 100 and white

reaches 40.95:1. However, the vast majority of those

4096 code values cannot be distinguished visually; for

example, codes 4001 through 4040 are visually indis-

tinguishable. Rather than coding luminance linearly

with a large number of bits, we can use many fewer

code values assigned nonlinearly on a perceptual scale.

If the threshold of vision behaved strictly according to

the 1% relationship across the whole tone scale, then

luminance could be coded logarithmically. For a con-

trast ratio of 100:1, about 463 code values would be

required, corresponding to about 9 bits. In video,

for reasons to be explained in Luminance and lightness,

on page 203, instead of modeling the lightness sensi-

tivity of vision as a logarithmic function, we model it as

a power function with an exponent of about 0.4.

Conversely, monitor R’G’B’ values

are proportional to reproduced

luminance raised to approximately

the 0.4-power.

The luminance of the red, green, or blue primary light

produced by a monitor is proportional to voltage (or

code value) raised to approximately the 2.5-power. This

will be detailed in Chapter 23, Gamma, on page 257.

The cathode ray tube (CRT) is the

dominant display device for tele-

vision receivers and for desktop

computers.

Amazingly, a CRT’s transfer function is nearly the

inverse of vision’s lightness sensitivity! The nonlinear

lightness response of vision and the power function

intrinsic to a CRT combine to cause monitor voltage, or

code value, to exhibit perceptual uniformity, as demon-

strated in Figures 1.17 and 1.18 overleaf.

∆ = 1%

100

101

4095

40.95 : 1

Figure 1.16 The “code 100”

problem is mitigated by using

more than 8 bits to represent

luminance. Here, 12 bits are

used, placing the top end of the

scale at 4095. However, the

majority of these 4096 codes

cannot be distinguished visually.

lg .

100

101

463 1 01 100

463

≈≈

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