The rapid advancements in technology in recent years have led to a massive increase in the exchange of data (images, videos, audio, etc.) between portable devices. This has invoked the necessity for building algorithms which consume low power with no compromise in the performance. In this paper, the above captioned issue is taken into account and accordingly an image compression technique using Repetitive Iteration CORDIC (RICO) architecture has been proposed. The proposed method is power efficient as it uses RICO for Discrete Cosine Transform (DCT) coefficient generation, and performs equally well when compared to Joint Photographic Experts Group (JPEG) standard. Results have been obtained via Matrix Laboratory (MATLAB) and they show that the proposed technique performs equally well and consumes less power in comparison with the other techniques.
Image Compression Using Permanent Neural Networks for Predicting Compact Discrete Cosine Transform Coefficients
