JPEG Image Compression with Signal-to-Noise Ratio Analysis

In this work, we can implement the following steps for image processing: First, perform JPEG compression on the image, then calculate and output the signal-to-noise ratio. This method effectively reduces image file size while maintaining reasonable image quality levels. During JPEG compression, we can utilize various compression parameters and algorithms to control the compression ratio and image clarity. The implementation typically involves using libraries like Python's PIL/Pillow or OpenCV, where key functions like `save()` with quality parameters control the compression level. Signal-to-noise ratio serves as a crucial metric for evaluating image quality, measuring the proportion between meaningful image signals and compression-induced noise. This helps assess image clarity and detail loss after compression. In code implementation, SNR calculation involves comparing original and compressed images pixel-wise using formulas like MSE (Mean Squared Error) and PSNR (Peak Signal-to-Noise Ratio), which can be implemented through matrix operations or dedicated image quality assessment functions. Therefore, in image processing workflows, performing JPEG compression with SNR output represents an essential step for balancing file size reduction and quality preservation. The process can be automated through scripts that systematically test different compression levels and generate quality reports, making it valuable for applications requiring optimized image storage and transmission.