Simple Implementation of Differential Pulse Code Modulation (DPCM) for Image Prediction Coding

During my undergraduate project, I developed a simple Differential Pulse Code Modulation (DPCM) program for image encoding, designed to reduce the number of bits required for data transmission and storage. The implementation involved calculating pixel-to-pixel differences using predictive coding techniques and encoding these differences to achieve compression. This approach typically involves scanning the image row by row, predicting each pixel value based on preceding pixels (commonly using left-neighbor prediction), and encoding the residual error between actual and predicted values using fewer bits through quantization. The project provided me with deep insights into signal processing principles and enhanced my understanding of data compression methodologies. During implementation, I faced several challenges including handling edge cases for border pixels, optimizing quantization levels to balance compression ratio and image quality, and managing computational efficiency. The final successful implementation demonstrated the practical application of DPCM algorithms, which typically involve key functions like prediction error calculation, quantization table design, and entropy coding integration. This project established a solid foundation for my future academic research and professional development in digital signal processing and compression technologies, particularly in understanding how predictive coding reduces spatial redundancy in image data. The core algorithm implementation included modular components for image preprocessing, difference calculation, and compressed data representation.