Implementation Challenges and Performance Analysis of Image Compression Using Huffman Encoding and DCT Algorithm on DSP Processor TMS320C6748 and Arduino Nano 33 BLE

To strike a compromise between compression ratio and image quality, this work focuses on a complete approach to image compression that combines Huffman coding (HC) and discrete cosine transform (DCT). The mathematical underpinnings of DCT and practical applications are examined, with a focus on how well it reduces redundancy. To further improve compression effectiveness, the project incorporates the entropy coding process known as Huffman coding after the DCT phase. By giving shorter codes to symbols that appear frequently, Huffman coding enhances the representation of altered visual data. The paper covers optimization strategies and design concerns for using Huffman coding with Discrete Cosine Transform. The study evaluates image compression techniques employing DCT and HC transforms to determine superior outcomes in terms of peak signal-to-noise ratio (PSNR), compression ratio, and root mean square error (RMSE) and to perform image compression efficiently.