Pertemuan 2: Citra Digital, Sampling, dan Quantization-Part 5: Konsep dasar Sampling & Quantization

Summary

The video discusses the concepts of sampling and quantization in digital image processing. It explains how continuous natural images, which are continuous in both spatial coordinates (x and y) and light intensity, are converted into digital form through sampling (spatial) and quantization (amplitude). The video highlights that the quality of digital images depends on factors like the number of samples, intensity levels, and the presence of noise. Different sensor types for capturing samples are also discussed, alongside how the sampled data is represented digitally in arrays or matrices.

Takeaways

• 📷 Image conversion: Continuous natural images are converted into digital form through sampling and quantization.
• 🌐 Spatial sampling: Spatial coordinates (x and y) of continuous images are sampled for digitization.
• 🎚️ Amplitude quantization: The amplitude of intensity values is quantized to discrete levels for digital representation.
• 🔍 Noise impact: The accuracy of quantization depends on the presence or absence of noise in the sampled signal.
• 🎛️ Sampling limitations: Image quality depends on the number of samples and intensity levels, as well as noise.
• 📈 1D function representation: The continuous function of intensity along a line (AB) is sampled and quantized to generate a 1D image.
• 🧩 Multi-dimensional sampling: The sampling and quantization process extends to 2D images by repeating the steps for multiple spatial coordinates.
• 🔢 Quantization levels: A fixed number of intensity levels (e.g., 8 levels) is used for quantization, limiting the grayscale variations in the final image.
• 🎥 Sensor types: Different sensor configurations (single, strip, or area sensors) are used for sampling, with each type affecting the sampling method and image quality.
• 📊 Digital image representation: The sampled and quantized data is stored as a 2D array where each element represents the intensity at a specific spatial coordinate.

Q & A

What is the key purpose of sampling and quantization in image processing?

Sampling and quantization are used to convert a continuous natural image into a digital form. Sampling involves discretizing the spatial aspect of an image, while quantization deals with converting the continuous amplitude (intensity) values into discrete levels.

How does noise affect the quantization process?

Noise can significantly affect the accuracy of quantization. If an image contains noise during the sampling process, it can degrade the quality of the quantized image by introducing errors in intensity levels, leading to a less accurate digital representation.

What is the role of spatial coordinates in the sampling process?

Spatial coordinates, typically denoted by x and y, represent the positions in the image where sampling is performed. The continuous image is sampled at specific spatial points along these coordinates to create a discrete digital version.

What is the difference between sampling and quantization?

Sampling refers to selecting discrete points from a continuous spatial domain (x and y coordinates of an image), while quantization refers to converting continuous amplitude values (intensities) into a finite number of discrete levels.

Why is it important to decide on the number of quantization levels?

The number of quantization levels directly influences the accuracy of the digital representation of an image. More levels provide finer intensity differentiation, leading to a higher-quality image, whereas fewer levels may cause loss of detail.

How does the presence of noise in an image influence the quality of the digital image produced?

Noise introduces irregularities in the intensity values of the image. These irregularities can interfere with the accuracy of sampling and quantization, reducing the quality of the final digital image by distorting the intended intensity values.

What is a single sensor setup, and how does it work in the sampling process?

A single sensor setup uses one sensor element to capture samples from the image. The sensor is moved mechanically to collect samples from different positions, allowing it to gather spatial data incrementally across the image.

What are the advantages and limitations of using a strip sensor for sampling?

A strip sensor consists of multiple sensor elements arranged in a line, capturing data across a specific dimension without requiring mechanical movement for that direction. However, it limits sampling to the number of sensors in the strip and relies on mechanical motion in the other direction.

How are image data represented digitally after sampling and quantization?

After sampling and quantization, image data are represented as a 2D array or matrix, where each element corresponds to a sampled point in the image. The intensity value at each point is the quantized intensity level, representing the brightness or color.

What is the significance of choosing the bit depth (e.g., 8-bit) for digital images?

The bit depth determines the number of intensity levels available for each pixel in the image. For example, an 8-bit image has 256 levels of intensity, allowing finer differentiation in grayscale or color values, which improves image quality and detail representation.