Multiple image compression cases based on Matlab (attach source code + data set)

Image compression is a technology that reduces the amount of image data to reduce storage space and transmission bandwidth requirements. In this article, we will introduce how to implement image compression using Matlab.

simple case

First, we need to understand the two main methods of image compression: lossy and lossless. Lossy compression refers to the loss of some image details during the compression process, thereby reducing the amount of data. Lossless compression means that no image details are lost during the compression process.

In Matlab, we can use the functions in the image processing toolbox to achieve image compression. The following is a basic image compression process:

1. Read image: Use the imread function to read the image that needs to be compressed. For example, we can read an image named "image.jpg" with the following code:
   image = imread('image.jpg');

2. Select a compression method: Select a lossy or lossless compression method according to your needs. For lossy compression, functions such as imresize, imwrite, etc. can be used. For lossless compression, functions such as imwrite, imfinfo, etc. can be used.

3. Set compression parameters: Set the corresponding parameters according to the requirements of the compression method. For example, for the imresize function, you can set the compression ratio; for the imwrite function, you can set the compression format.

4. Perform Compression: Perform image compression according to the selected compression method and parameters. For example, a code example for lossy compression using the imresize function is as follows:
   compressed_image = imresize(image, 0.5);

5. Save the compressed image: Use the imwrite function to save the compressed image to the specified file. For example, use the following code to save the compressed image as "compressed_image.jpg":
   imwrite(compressed_image, 'compressed_image.jpg');

Through the above steps, we can achieve basic image compression. However, to implement more advanced image compression algorithms, it may be necessary to use more functions and techniques. For example, a JPEG compression algorithm can be implemented using discrete cosine transform (DCT) and quantization.

To sum up, using Matlab to achieve image compression can be done by selecting a compression method, setting parameters, performing compression and saving the compressed image. By using the functions in Matlab's image processing toolbox, we can implement different types of image compression, including lossy compression and lossless compression. I hope this article can help you understand the implementation of image compression in Matlab.

Source code + data set download

Multiple image compression cases based on Matlab (source code + data set).rar: https://download.csdn.net/download/m0_62143653/88189909