Calculating Root Mean Square Error and Peak Signal-to-Noise Ratio for Compressed-Decompressed Images

In this experiment, we aim to develop a universal program for calculating root mean square error and peak signal-to-noise ratio of compressed-decompressed images. The program will generate visualized results and employ the aforementioned fidelity criteria to compute visual information loss characteristics. To achieve this objective, we need to consider the following aspects: First, selection of compression algorithms: We can explore different compression techniques such as JPEG, PNG, etc., and compare their performance in terms of RMSE and PSNR metrics. The implementation would involve using standard libraries like PIL for JPEG compression or OpenCV for PNG processing, with parameter tuning for compression ratios. Second, implementation of decompression algorithms: We need to develop decompression routines that accurately restore compressed images to their original state with minimal error introduction. This requires careful handling of color space conversions and proper implementation of inverse transformation algorithms using libraries such as scipy or custom wavelet reconstruction functions. Third, result visualization: To better understand information loss during compression-decompression processes, we can compare pre- and post-compression images using side-by-side displays and graphically represent RMSE/PSNR variations through matplotlib visualization. This includes generating heat maps for error distribution and creating comparative histograms for pixel value changes. Through these enhancements, we can comprehensively understand the image compression-decompression pipeline and effectively evaluate image quality degradation characteristics using quantitative metrics and visual comparisons. The code implementation would typically involve image I/O operations, matrix calculations for error metrics, and interactive plotting functionalities.