Image Compression Using Linear Predictive Coding

This paper implements image compression using Linear Predictive Coding (LPC), which is a lossless compression technique. Linear Predictive Coding is a prediction-based data compression method that reduces data volume by encoding the differences between predicted pixel values and actual pixel values. The algorithm works by establishing a predictive model to forecast subsequent pixel values, then encoding only the residual errors between predictions and actual values. From an implementation perspective, LPC typically involves calculating weighted sums of previous pixels using coefficients derived from autocorrelation analysis. Key functions would include: - Computing prediction coefficients through Levinson-Durbin recursion or similar algorithms - Implementing difference encoding for residual errors - Applying entropy coding to the residual data for further compression This method is widely used in image compression applications and can significantly reduce image file sizes while maintaining original image quality. Linear Predictive Coding serves as an efficient image compression algorithm that has found extensive applications across various domains including medical imaging and digital photography. The technique's effectiveness stems from its ability to exploit spatial redundancy in image data through sophisticated predictive modeling.