A Low-Power Area-Efficient Design and Comparative Analysis for High-Resolution Neural Data Compression

Nowadays, brain scientific research progress depends on signal compression at high spatial resolutions, for efficient storage and low-rate transmission through wireless connection to outside world. So that neural data compression at the implant site is necessary in order to conform with the wireless rates restrictions. In this paper, the high spatial correlation is utilized to increase the data compression ratio. Then we investigate and compare three different proposed low-power image compression algorithms based on discrete cosine transform (DCT) and discrete wavelet transform (DWT) to provide the best trade-off between hardware complexity and compression performance. Hence, we conclude that Adaptive 2D-DWT algorithm is a promising solution for low-power implantable devices.