【Practical Exercise】Image RGB and HSV Distribution Based on MATLAB

# 2.1 Composition and Characteristics of RGB Images

An RGB image, also known as a true color image, is composed of three channels: Red, Green, and Blue. Each channel corresponds to a byte, hence each pixel point in an RGB image requires 3 bytes for storage. The characteristics of RGB images are as follows:

- **Primary Colors Model:** RGB images are based on the primary colors model, which includes red, green, and blue. These three colors can be mixed to produce a wide variety of colors, including white, black, and shades of gray.
- **Color Space:** The color space of an RGB image is a cube, where each vertex represents a primary color, the center represents black, and white lies outside the cube.
- **Brightness and Chroma:** The brightness of an RGB image is determined by the average value of the three channels, while the chroma is determined by the relative proportions of the three channels.
- **Human Eye Perception:** RGB images match the way the human eye perceives colors, thus being able to realistically represent the colors of the natural world.

# 2. RGB Image Analysis and Processing

### 2.1 Composition and Characteristics of RGB Images

RGB images are an image format based on the primary colors red (Red), green (Green), and blue (Blue). Each pixel is composed of three components, representing the intensity values of the pixel in the red, green, and blue channels, respectively. The characteristics of RGB images are as follows:

- **Three-Channel Model:** RGB images use three channels to represent color information, each corresponding to one primary color.
- **Additive Color Model:** RGB images employ an additive color model, meaning various colors are produced by combining the three primary colors of red, green, and blue.
- **Wide Application:** RGB image format is widely used in displays, cameras, and image processing software.

### 2.2 Reading, Writing, and Displaying RGB Images

**Reading and Writing RGB Images**

MATLAB provides various functions for reading and writing RGB images, some of which include:

% Reading an RGB image
rgbImage = imread('image.jpg');

% Writing an RGB image
imwrite(rgbImage, 'new_image.jpg');

**Displaying RGB Images**

The `imshow` function can be used to display RGB images:

% Displaying an RGB image
imshow(rgbImage);

### 2.3 Conversion of RGB Image Color Spaces

RGB images can be converted into other color spaces, such as HSV, YCbCr, etc. Color space conversion can be used for tasks such as image enhancement, color correction, and feature extraction.

**Conversion from RGB to HSV**

The HSV (Hue, Saturation, Value) color space is based on human visual perception. It consists of three components: Hue (H), Saturation (S), and Value (V).

% Conversion from RGB to HSV
hsvImage = rgb2hsv(rgbImage);

**Conversion from RGB to YCbCr**

The YCbCr (Luminance, Chrominance Blue, Chrominance Red) color space is used for video compression and broadcasting. It includes a luminance component (Y) and two chrominance components (Cb and Cr).

% Conversion from RGB to YCbCr
ycbcrImage = rgb2ycbcr(rgbImage);

**Applications of Color Space Conversion**

Color space conversion has a wide range of applications in image processing, such as:

- **Image Enhancement:** By adjusting the component values in different color spaces, image contrast, saturation, and brightness can be enhanced.
- **Color Correction:** Color space conversion can be used to correct color distortions in images, such as white balance and hue adjustments.
- **Feature Extraction:** Different color spaces can extract different image features for tasks such as image classification and object detection.

# 3.1 Composition and Characteristics of HSV Images

HSV images (Hue, Saturation, Value) are a nonlinear color space that is closer to how humans perceive colors. Unlike RGB images, HSV images separate the hue, saturation, and brightness of colors into three independent channels.

**Hue:** Represents the purity of the color, ranging from 0 to 360 degrees. 0 degrees represent red, 120 degrees represent green, 240 degrees represent blue, and 360 degrees loop back to red.

**Saturation:** Represents the vividness of the color, ranging from 0 to 1. 0 indicates full saturation, while 1 indicates complete unsaturation (gray).

**Value (Brightness):** Represents the lightness or darkness of the color, ranging from 0 to 1. 0 indicates white, while 1 indicates black.

The advantages of HSV images include:

***Intuitiveness:** The HSV model is closer to how humans perceive colors, making it easier