science

Image processor

Image processor is a 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 Image processor rather than just read about it. In short: An image processor, also known as an image processing engine, image processing unit (IPU), or image signal processor (ISP), is a type of media processor or specialized digital signal processor (DSP) used for image processing, in digital cameras or other devices. Image processors often employ parallel computing even with SIMD or MIMD technologies to increase speed and efficiency.

Key takeaways

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

Reference excerpt

An image processor, also known as an image processing engine, image processing unit (IPU), or image signal processor (ISP), is a type of media processor or specialized digital signal processor (DSP) used for image processing, in digital cameras or other devices. Image processors often employ parallel computing even with SIMD or MIMD technologies to increase speed and efficiency. The digital image processing engine can perform a range of tasks. To increase the system integration on embedded devices, often it is a system on a chip with multi-core processor architecture.

Function

Bayer transformation The photodiodes employed in an image sensor are color-blind by nature: they can only record shades of grey. To get color into the picture, they are covered with different color filters: red, green and blue (RGB) according to the pattern designated by the Bayer filter. As each photodiode records the color information for exactly one pixel of the image, without an image processor there would be a green pixel next to each red and blue pixel. This process, however, is quite complex, and involves a number of different operations. Its quality depends largely on the effectiveness of the algorithms applied to the raw data coming from the sensor. The mathematically manipulated data becomes the recorded photo file.

Demosaicing As stated above, the image processor evaluates the color and brightness data of a given pixel, compares them with the data from neighboring pixels, and then uses a demosaicing algorithm to produce an appropriate color and brightness value for the pixel. The image processor also assesses the whole picture to guess at the correct distribution of contrast. By adjusting the gamma value (heightening or lowering the contrast range of an image's mid-tones), subtle tonal gradations, such as in human skin or the blue of the sky, become much more realistic.

Noise reduction Noise is a phenomenon found in any electronic circuitry. In digital photography its effect is often visible as random spots of obviously wrong color in an otherwise smoothly-colored area. Noise increases with temperature and exposure times. When higher ISO settings are chosen the electronic signal in the image sensor is amplified, which at the same time increases the noise level, leading to a lower signal-to-noise ratio. The image processor attempts to separate the noise from the image information and to remove it. This can be quite a challenge, as the image may contain areas with fine textures which, if treated as noise, may lose some of their definition.

Image sharpening As the color and brightness values for each pixel are interpolated some image sharpening is applied to even out any fuzziness that has occurred. To preserve the impression of depth, clarity and fine details, the image processor must sharpen edges and contours. It therefore must detect edges correctly and reproduce them smoothly and without over-sharpening.

Models Image processor users are using industry standard products, application-specific standard products (ASSP) or even application-specific integrated circuits (ASIC) with trade names: Canon's is called DIGIC, Nikon's Expeed, Olympus' TruePic, Panasonic's Venus Engine and Sony's Bionz. Some are known to be based on the Fujitsu Milbeaut, the Texas Instruments OMAP, Panasonic MN103, Zoran Coach, Altek Sunny or Sanyo image/video processors. ARM architecture processors with its NEON SIMD Media Processing Engines (MPE) are often used in mobile phones.

Processor brand names ATI - Imageon (graphics co-processor used in many early mobile phones to offer camera image signal processing) Canon - DIGIC (based on Texas Instruments OMAP) Casio - EXILIM engine Epson - EDiART Fujifilm - EXR III or X Processor Pro Google - Pixel Visual Core HTC - ImageSense Intel - IPU MediaTek - Imagiq Minolta / Konica Minolta - SUPHEED with CxProcess Leica - MAESTRO (based on Fujitsu Milbeaut) Nikon - Expeed (based on Fujitsu Milbeaut) Olympus - TruePic (based on Panasonic MN103/MN103S) OPPO - MariSilicon X Panasonic - Venus Engine (based on Panasonic MN103/MN103S) Pentax - PRIME (Pentax Real IMage Engine) (newer variants based on Fujitsu Milbeaut) Qualcomm - Qualcomm Spectra (based on Qualcomm Snapdragon) Ricoh - GR engine (GR digital), Smooth Imaging Engine Samsung - DRIMe (based on Samsung Exynos) Sanyo - Platinum engine Sigma - True Sharp - ProPix Socionext - Milbeaut Family of ISPs - SC2000 (M-10V), SC2002 (M-11S) Sony - Bionz THine - THP series [1] with compatible SDK Kit for developing firmware [2] UNISOC - Vivimagic

Speed With the ever-higher pixel count in image sensors, the image processor's speed becomes more critical: photographers don't want to wait for the camera's image processor to complete its job before they can carry on shooting - they don't even want to notice some processing is going on inside the camera. Therefore, image processors must be optimised to cope with more data in the same or even a shorter period of time.

Software libcamera is a software library that supports using image signal processors for the capture of pictures.

See also Color image pipeline Image processing Digital image processing Digital image editing Demosaicing

References

Illustrations

Worked examples

Example 1 — a first encounter with Image processor

Start with the simplest possible case. Write down what Image processor claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In 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 Image processor 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 Image processor 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 Image processor

In research: Image processor appears in 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 Image processor 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: Image processor is common in secondary-school and first-year university syllabi. It links to neighbouring topics Digital signal processors, Image processors, Photography equipment, so understanding it makes those chapters shorter.
In everyday life: Look for Image processor 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 Image processor in 20 minutes

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

Frequently asked questions

What is Image processor in simple terms?

Why does Image processor matter?

Because it connects several 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 Image processor?

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 Image processor.