DCT image compression implemented on raspberry Pi to compress image captured by CMOS image sensor

An image contains large amount of digital data and it is necessary to reduce digital data volume for transmission and preservation by using image compression. This project mainly concentrates on image compression using a Raspberry Pi processor, which helps to preserve a large number of images and in retaining its quality. Signal, Image and data compression are an important signal processing tool in many areas of research, such as medicine, biometric, telecommunication, automotive and. The purpose of compression is to reduce the amount of data at the same time maintain the quality of image and signal for the other purpose. DCT transform is a family of image compression where the raw image is transformed to the other domain to produce smaller size of data. DCT transform has low computational complexity and fast processing algorithm. In this project, DCT transform will be implemented using Raspberry Pi SBC development board running on an ARM based processor. The raspberry Pi board has an advantage of image processing implementation due to the existing software development tool offered a rich feature for image processing such as OPENCV. The project consists of several design stages such as image pre-processing, the development of DCT algorithm, error rate computation and measurement by PSNR and MSE. The algorithm is developed using Python programming language with the additional image processing library such as OpenCV and NumPy mathematical library. The development of Internet and multimedia technologies that grow exponentially, resulting in the amount of information managed by computer is necessary. This causes serious problems in storage and transmission image data on real time. Therefore, should be considered a way to compress data so that the storage capacity required will be smaller. In this research wanted to know the influence of DCT to the compression ratio and to the PSNR (Peak Signal to Noise Ratio). Then the compression ratio and PSNR results on real time provide excellent visual quality. The result of applying DCT compression algorithm on images with six compression rate level which are 10, 20, 50, 100,170 and 200. The best performance can be achieved with compression rate level 200. However, on reducing the quality level of compression rate, the error measurements start becoming worse until a point is reached, where the perceptual difference from the original image can be easily noted.