视频与音频压缩技术精要

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"《Compression for Great Video and Audio, Second Edition》是Ben Waggoner撰写的一本关于视频和音频压缩技术的专业书籍，由FocalPress（Elsevier的印记）出版。本书探讨了如何优化视频和音频的压缩技术，提供实用技巧和常识性指导，旨在帮助读者在数字媒体领域提升内容的质量和效率。" 在当今数字化的世界，视频和音频内容的压缩至关重要，因为它们直接影响到传输速度、存储需求以及最终用户体验。《Compression for Great Video and Audio》第二版深入浅出地讲解了这一领域的核心知识。本书可能涵盖了以下关键知识点： 1. **压缩原理**：介绍视频和音频数据压缩的基本原理，如熵编码、有损和无损压缩的区别，以及它们在实际应用中的优缺点。 2. **编码标准**：详细讨论了各种视频编码标准，如MPEG、H.264/AVC、HEVC（H.265）、VP9和AV1等，以及音频编码标准如MP3、AAC、Opus和FLAC等，解释其工作方式和特性。 3. **质量与码率控制**：阐述如何在保持高质量的同时减少码率，通过比特率适应、量化参数调整等手段平衡文件大小和视觉/听觉效果。 4. **编解码器优化**：讨论如何选择和配置合适的编解码器以实现最佳性能，包括硬件加速和多核处理的利用。 5. **实时流传输**：分析实时视频和音频流的压缩挑战，如延迟、丢包恢复策略以及适应不同网络条件的技术。 6. **文件格式**：介绍不同类型的视频和音频文件格式，如MP4、MOV、MKV等，以及它们在压缩、封装和互操作性方面的差异。 7. **版权保护**：提及了版权问题，提示读者在使用和传播内容时需遵循版权法规，提供了获取使用许可的相关信息。 8. **技术更新**：强调了知识和最佳实践的不断变化，随着新研究和经验的发展，可能需要更新压缩方法、专业实践或医疗技术。 9. **实践应用**：书中可能包含实际案例和工作流程，指导读者将理论知识应用于实际项目，解决具体问题。 10. **未来趋势**：展望了视频和音频压缩的未来发展方向，如高动态范围（HDR）、超高清（UHD）和8K分辨率等新兴技术。此书对于视频和音频制作人员、多媒体开发人员、网络工程师以及任何关心媒体质量和效率的读者来说，都是一份宝贵的参考资料。通过阅读，读者不仅能了解压缩技术的理论基础，还能掌握提高内容质量和效率的实际技巧。

4 Chapter 1

We’re not equally sensitive to all colors, however. We’re most sensitive to green, less

to red, and least of all to blue. Really. Trust me on this. This is going to come up again

and again, and is the fundamental reason why we use green screens for “blue screen”

video these days, and why red subtitles can get so blocky when going out to DV tape.

Late-Night College Debate Settled Once and for All!

So, if you were prone to senseless philosophizing at late hours as I once was (having

children and writing books seem to have effectively displaced that niche in my

schedule), you’ve no doubt heard speculation ad nauseam as to whether or not what

one of us calls “red” could be what another perceives as “blue.”

Kudos if you’ve never thought about this, because Color Figure C.2 shows that what

you see as red is what every other person with normal version perceives as red. In every

culture in the world, in any language, if you ask what the most “red” color is and offer

a bunch of samples, almost everyone picks the sample at 564 nm. Thus what you see as

red, I see as red, and so does everyone else. The same is true for green and blue. I ﬁnd it

rather pleasing when something that once seemed abstract and unknowable turns out

to have an extremely concrete answer like this. I’ll be calling out other examples of life’s

mysteries answered by compression obsession throughout the book.

Also, not everyone has all three types of cones. About seven percent of men (and a much

smaller percentage of women) have only two of the three cone types, causing red-green color

blindness, the most common type (see Color Figure C.3). Dogs and most other mammals also

have only two cone types. Not that it’s that much of a problem—many people with red-green

color blindness don’t even know until they get their vision tested for a driver’s license.

Red-green color blindness is also why my dad wears rather more pink than he realizes. It’s

not all bad news, though. People with red-green color blindness are much better at seeing

through color camouﬂage that confuses the three-coned among us.

Another aspect of the retina is that most of the detail we perceive is picked up within a very

small area of what the eye can see, in the very center of the visual ﬁeld, called the fovea.

The outer portion of the retina consists mainly of rods for low-light and high-speed vision.

The critical cones are mainly in the center of the retina. Try staring directly at a speciﬁc

point on a wall without letting your eyes move around, and notice how small a region is

actually in focus at any given time. That small region of clear vision is what the fovea sees –

it covers only 1% of the retina, but about 50% of the visual parts of the brain are devoted to

processing information from the fovea. Fortunately, our eyes are able to ﬂick around many

times a second, pointing the fovea at anything interesting. And we’ve got a big chunk of

brain that’s there for ﬁlling in the gaps of what we aren’t seeing. At any given time, much

Seeing and Hearing 5

of what we think we’re seeing we’re actually imagining, ﬁlling in the blanks from what our

brain remembers our eyes were looking at a fraction of a second ago, and guessing at what

normally would happen in the environment.

How the Brain Sees

It’s hard to draw a real mind/body distinction for anything in neuroscience, and this is no

less true of vision. What we call “seeing” takes place partially in the retina and partially in

the brain, with the various systems interacting in complex ways. Of course, our sense of

perception happens in the brain, but some basic processing such as ﬁnding edges actually

starts in the retina. The neuroscience of vision has been actively researched for decades, but

it’s impossible to describe how vision works in the brain with anywhere near the speciﬁcity

we can in the eye. It’s clear that the visual system takes up about a quarter of the total human

brain (that’s more than the total brain size of most species). Humans have the most complex

visual processing system of any animal by a large margin.

Under ideal circumstances, humans can discriminate between about one million colors.

Describing how might sound like advanced science, but the CIE (Commission Internationale de

l’Eclairage—French for International Commission on Illumination) had it mostly ﬁgured out

back in 1931. The CIE determined that the whole visible light spectrum can be represented as

mixtures of just red, green, and blue. Their map of this color space (Color Figures C.4 and C.5)

shows a visible area that goes pretty far in green, some distance in red, and not far at all in blue.

However, there are equal amounts of color in each direction; the threshold between a given

color and black shows how much color must be present for us to notice. So we see green pretty

well, red so-so, and blue not very well. Ever notice how hard it is to tell navy blue from black

unless it’s viewed in really bright light? The reason is right there in the CIE chart.

Another important concept that the CIE incorporated is “luminance”—how bright objects

appear. As noted previously, in normal light humans don’t actually see anything in black and

white; even monochrome images start with the three color receptors all being stimulated.

But our brain processes brightness differently than it does color, so it’s very important to

distinguish between them. And optimizing for that difference is central to how compression

works, and even how color television was implemented a half-century ago. We’ll be talking

about how that difference impacts our media technologies throughout the book.

Software sometimes turns RGB values into gray by just averaging the three values. However,

this does not match the human eye’s sensitivity to color. Remember, we’re most sensitive

to green, less to red, and least to blue. Therefore, our perception of brightness is mainly

determined by how much green we see in something, with a good contribution from red, and

a little from blue.

For math fans, here’s the equation. We call luminance Y because…I really don’t know why.

Y Green Red Blue   0 587 0 299 0 114. . .

6 Chapter 1

Note that this isn’t revealing some fundamental truth of physics; it’s an average based on

testing of many human eyes. Species with different photoreceptors would wind up with

different equations, and there can be some variation between individuals, and quite a lot

between species.

Encoding images requires representing analog images digitally. Given our eyes’ lack of

sensitivity to some colors and our general lack of color resolution, an obvious way to

optimize the data required to represent images digitally is to tailor color resolution to the

limitations of the human eye.

Color vision is enormously cool stuff. I’ve had to resist the temptation to prattle on and on

about the lateral geniculate nucleus and the parvocellular and magnocellular systems and

all the neat stuff I learned getting my BA in neuropsychology. (At my ﬁfteenth reunion, I

bumped into the then and still Dean of Hampshire’s School of Cognitive Science. He asked

why I’d left academic neuropsychology for something so different as putting video on

computers. My defense?: “Video compression is just highly applied neuropsychology.”)

How We Perceive Luminance

Even though our eyes work with color only at typical light levels, our perception is much

more tuned for luminance than color. Speciﬁcally, we can see sharp edges and ﬁne detail in

luminance much better than we can in color. We can also pick out objects moving in a manner

different from other objects around them a lot more quickly, which means we can also notice

things that are moving better than things standing still. In optimal conditions with optimal

test material, someone with good vision is able to see about 1/60th of a degree with the center

of their fovea—the point where vision is most acute. That’s like a dot 0.07 inches across at a

distance of 20 feet.

A lot of the way we see is, fundamentally, faking it. Or, more gracefully put, our visual

system is very well attuned to the sort of things bipedal primates do to survive on planet

Earth, such as ﬁnding good things to eat without being eaten ourselves. But given images

that are radically different from what exists in nature, strange things can happen. Optical

illusions take advantage of how our brains process information to give illusory results—a

neuropsychological hack, if you will. Check out Figure 1.3. Notice how you see a box that

isn’t there? And how the lines don’t look quite straight, even when they are?

How We Perceive Color

Compared to luminance, our color perception is blurry and slow. But that’s really okay. If a

tiger jumped out of the jungle, our ancestors didn’t care what color stripes it had, but they did

need to know where to throw that spear now. Evolutionarily, color was likely important for

discriminating between poisonous and yummy foods, an activity that doesn’t need to happen

in a split second or require hugely ﬁne detail in the pattern of the colors themselves.

8 Chapter 1

As you can see in Color Figure C.7, our perception of what white is varies a lot with context.

It also varies a lot with the color of the ambient light that you’re looking at the image in.

We aren’t normally aware of the fact that white can vary so much. Our brains automatically

calibrate our white perception, so that the same things appear to have the same color under

different lighting, despite the relative frequencies of light bouncing off them and into our

eyes varying wildly. This is one reason why white balancing a camera manually can be so

difﬁcult—our eyes are already doing it for us.

How We Perceive Space

Now you understand how we see stuff on a ﬂat plane, but how do we know how far things

are away from us? There are several aspects to depth perception. For close-up objects, we

have binocular vision, because we have two eyes. The closer an object is to us, the more

different the image we see with each eye, because they’re looking at the object at different

angles. We’re able to use the degree of difference to determine the distance of the object.

But this works only up to 20 feet or so. And you can still have a good idea of where

things are even with one eye covered. Binocular vision is important, but it isn’t the

whole story.

Alas, we don’t know all of the rest of the story. There are clearly a lot of elements in what

we see that can trigger our perception of space. These include relative size, overlapping,

perspective, light and shading, blue shading in faraway objects, and seeing different angles of

the object as you move your head relative to the object. See Figure 1.4 for how a very simple

image can yield a sense of perspective and Figure 1.5 (also Color Figure C.8) for how a

combination of cues can. You don’t need to worry about elements that contribute to our depth

perception too much for compression, except when compression artifacts degrade an element

so much that the depth cues are lost. This is mainly a problem with highlights and shadows in

the image.

Figure 1.4 This simple grid provides a powerful sense of perspective.

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