1.
What is an Analog Image?
2.
Analog Image to Digital Image Conversion
3.
What is Image Sampling?
4.
What is Image Quantization?
5.
Difference Between Image Sampling and Quantization
6.
Advantages of Image Sampling and Quantization
7.
Disadvantages of Image Sampling and Quantization
8.
Applications of Image Sampling and Quantization
9.
9.1.
What is 2d sampling of an image?
9.2.
What is image sampling frequency?
9.3.
Why do we need image sampling?
9.4.
What is sampling theory in image processing?
10.
Conclusion
Last Updated: Jun 19, 2024
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# Difference Between Image Sampling and Quantization

Rahul Singh
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Master Python: Predicting weather forecasts
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Ashwin Goyal
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Images have become an indispensable part of our lives. We tend to take photographs of every occasion to remember them. But the pictures that we take are mostly analog images. We cannot process or store these analog images on our computers. Digital images are more useful than analog images. We can store them on computers, apply digital image processing, make hundreds and thousands of copies, and share them over the internet.

Collecting data from an analog source such as a photograph or video at various intervals is known as Sampling. Digital images are then generated by recreating the analog source from the collected samples. The color space, usually defined by its range or gamut is used to represent digital images. whereas quantization maps the sampled values to a finite set of levels, reducing their precision. Sampling deals with time or space intervals, while quantization addresses amplitude or value resolution.

First, let us see how to convert these analog images into digital images.

## What is an Analog Image?

When we capture the image of an object, we use image sensors to sense the incoming light and form the image. Image sensors convert the incoming light from an object into electrical signals that can be stored and viewed later. These analog signals are continuous. The images are stored in an analog form. Thus, the image formed has continuous variation in the tone.

We cannot process analog images by a computer. Analog signals contain infinite points, and we need infinite memory to store them. We need to convert the analog images into digital images to store and process by a computer.

## Analog Image to Digital Image Conversion

An analog image is converted to a digital image by digitizing the analog signals. We apply sampling and quantization to the analog signals to convert them into digital form.

A digital image is formed by arranging pixels in rows and columns. Each pixel has a particular integral value. The computer process that integral value and show us that pixel, the arrangement of the pixels form the digital image.

We use sampling and quantization to change the continuous analog image into quantized integral values that will represent each pixel and ultimately form the digital image.

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## What is Image Sampling?

Sampling is the process of converting an analog signal into discrete values. In layman's terms, we can say that sampling is the process of recording an analog signal at regular intervals of time. A sampling function is applied to the analog signal that results in the sampled signal.

We get a finite number of samples of an analog signal. The number of samples gives us the number of pixels. More samples will result in higher image quality of the digital image because of more pixels.

The sampled signal is then quantized to get the value of each pixel. Let us look at how quantization is achieved.

## What is Image Quantization?

After sampling the analog signal, we will apply quantization. Quantization digitizes the amplitude of the sampled signal. Quantization is done by rounding off the amplitude of each sample and then assigning a different value according to its amplitude. Each value will represent a different color tone.

Let us look at how different valued pixels form a grayscale image:

Each pixel is assigned an integer value after quantization. Each number represents a different shade of grey. The collection of these pixels will form the image. In the above example, there are 256 quantization level

## Difference Between Image Sampling and Quantization

Let's see how sampling and quantization differ:

## Advantages of Image Sampling and Quantization

• Improved Resolution: Sampling increases the resolution of digital images, resulting in sharper and clearer visuals.
• Color Depth Control: Quantization allows for precise control over color depth, enabling the creation of vibrant and richly colored images.
• Data Compression: Sampling and quantization facilitate data compression techniques, reducing storage requirements for images while maintaining visual quality.
• Standardization: Both processes allow for standardization of image formats and quality, ensuring compatibility across different devices and platforms.

## Disadvantages of Image Sampling and Quantization

• Loss of Information: Sampling and quantization may lead to the loss of subtle details and nuances present in the original analog signal or image.
• Artifacts: Improper sampling and quantization can introduce artifacts such as aliasing, banding, and color quantization errors, degrading image quality.
• Processing Overhead: Both processes require computational resources and processing time, especially for high-resolution images or complex color palettes.
• Limited Dynamic Range: Quantization can result in a loss of dynamic range, especially in low-bit-depth images, leading to loss of color fidelity and tonal gradations.

## Applications of Image Sampling and Quantization

• Digital Photography: Sampling and quantization are fundamental processes in digital photography, enabling the capture and processing of images from digital cameras and sensors.
• Medical Imaging: Sampling and quantization are used in medical imaging techniques such as MRI, CT scans, and ultrasound to convert analog signals into digital images for diagnosis and analysis.
• Remote Sensing: Image sampling and quantization are employed in satellite imagery and remote sensing applications to capture and process data from Earth's surface for environmental monitoring, urban planning, and agricultural analysis.
• Graphics and Multimedia: Sampling and quantization are essential in graphics and multimedia applications for rendering, compression, and transmission of images in computer graphics, video streaming, and virtual reality systems.

### What is 2d sampling of an image?

2D sampling of an image involves selecting a subset of pixels across the image's two-dimensional space to reduce its resolution or size while attempting to retain significant visual information, often used in image processing and compression techniques.

### What is image sampling frequency?

Image sampling frequency, often referred to as pixel sampling or pixel density, is the number of samples (pixels) taken per unit of distance in a digital image. It determines image resolution and quality.

### Why do we need image sampling?

Image sampling is needed because it allows you to convert continuous visual data into a digital format. This makes it easy for computers to process and display images accurately. Sampling defines the image resolution and detail, which affects its quality.

### What is sampling theory in image processing?

Sampling theory in image processing is the concept of converting a continuous image into a discrete representation by selecting and storing values at specific sample points. It underlies digital image formation and processing.

## Conclusion

In this article, you learned about Image Sampling and Quantization. We discussed about analog images and how they are converted to the digital format. We also looked at images describing sampling and quantization. At the end, we discussed the differences between the two terms.