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Transform coding

Transform coding is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Transform coding rather than just read about it. In short: Transform coding is a type of data compression for "natural" data like audio signals or photographic images. The transformation is typically lossless (perfectly reversible) on its own but is used to enable better (more targeted) quantization, which then results in a lower quality copy of the original input (lossy compression).

Key takeaways

  • Transform coding belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Transform coding to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Transform coding from memory before moving on to harder problems.

Reference excerpt

Transform coding is a type of data compression for "natural" data like audio signals or photographic images. The transformation is typically lossless (perfectly reversible) on its own but is used to enable better (more targeted) quantization, which then results in a lower quality copy of the original input (lossy compression). In transform coding, knowledge of the application is used to choose information to discard, thereby lowering its bandwidth. The remaining information can then be compressed via a variety of methods. When the output is decoded, the result may not be identical to the original input, but is expected to be close enough for the purpose of the application.

Colour television

NTSC One of the most successful transform encoding system is typically not referred to as such—the example being NTSC color television. After an extensive series of studies in the 1950s, Alda Bedford showed that the human eye has high resolution only for black and white, somewhat less for "mid-range" colors like yellows and greens, and much less for colors on the end of the spectrum, reds and blues. Using this knowledge allowed RCA to develop a system in which they discarded most of the blue signal after it comes from the camera, keeping most of the green and only some of the red; this is chroma subsampling in the YIQ color space. The result is a signal with considerably less content, one that would fit within existing 6 MHz black-and-white signals as a phase modulated differential signal. The average TV displays the equivalent of 350 pixels on a line, but the TV signal contains enough information for only about 50 pixels of blue and perhaps 150 of red. This is not apparent to the viewer in most cases, as the eye makes little use of the "missing" information anyway.

PAL and SECAM The PAL and SECAM systems use nearly identical or very similar methods to transmit colour. In any case both systems are subsampled.

Digital The term is much more commonly used in digital media and digital signal processing. The most widely used transform coding technique in this regard is the discrete cosine transform (DCT), proposed by Nasir Ahmed in 1972, and presented by Ahmed with T. Natarajan and K. R. Rao in 1974. This DCT, in the context of the family of discrete cosine transforms, is the DCT-II. It is the basis for the common JPEG image compression standard, which examines small blocks of the image and transforms them to the frequency domain for more efficient quantization (lossy) and data compression. In video coding, the H.26x and MPEG standards modify this DCT image compression technique across frames in a motion image using motion compensation, further reducing the size compared to a series of JPEGs. In audio coding, MPEG audio compression analyzes the transformed data according to a psychoacoustic model that describes the human ear's sensitivity to parts of the signal, similar to the TV model. MP3 uses a hybrid coding algorithm, combining the modified discrete cosine transform (MDCT) and fast Fourier transform (FFT). It was succeeded by Advanced Audio Coding (AAC), which uses a pure MDCT algorithm to significantly improve compression efficiency. The basic process of digitizing an analog signal is a kind of transform coding that uses sampling in one or more domains as its transform.

See also Karhunen–Loève theorem Transformation (function) Wavelet transform

References

Worked examples

Example 1 — a first encounter with Transform coding

Start with the simplest possible case. Write down what Transform coding claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Transform coding before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Transform coding ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Transform coding

In research
Transform coding appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Transform coding in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Transform coding is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data compression, Lossy compression algorithms, so understanding it makes those chapters shorter.
In everyday life
Look for Transform coding outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Transform coding in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Transform coding means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Transform coding out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Transform coding in simple terms?

Transform coding is a type of data compression for "natural" data like audio signals or photographic images. The transformation is typically lossless (perfectly reversible) on its own but is used to enable better (more targeted) quantization, which then results in a lower quality copy of the origin…

Why does Transform coding matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Transform coding?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Transform coding.

Tags

  • Data compression
  • Lossy compression algorithms

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