Content Driven On-Chip Compression and Time Efficient Reconstruction for Image Sensor Applications

TLDR: The experiment shows that the compression of 86.2% can be achieved using the threshold of two intensity levels and the compressed image can be reconstructed with the PSNR of 45.87 dB.

Abstract: A superpixel based on-chip compression is proposed in this paper. Pixels are compared in spatial domain and the pixels with similar characteristics are grouped to form the superpixels. Only one pixel corresponding to each superpixel is read to achieve the compression. The on-chip compression circuit is designed and simulated in UMC 180 nm CMOS technology. For 70% compression, the proposed design results in about 33% power saving. The reconstruction of the compressed image is performed off-chip using bilinear interpolation. Further, two enhancement approaches are developed to improve the output image quality. The first approach is based on wavelet decomposition whereas the second approach uses a deep convolutional neural network. The proposed reconstruction technique takes two orders of magnitude lesser time as compared to the state-of-the-art technique. On an average, it results in peak signal to noise ratio (PSNR) and structural similarity index measure values of 30.999 and 0.9088 dB, respectively, for 70% compression in natural images. On the other hand, the best values observed from the existing approaches for the two metrics are 28.634 and 0.8115 dB, respectively. Further, the proposed technique is found useful for thermal image compression and reconstruction. The experiment shows that the compression of 86.2% can be achieved using the threshold of two intensity levels and the compressed image can be reconstructed with the PSNR of 45.87 dB.