Indian Journal of Science and Technology

Abstract

Background/Objectives: The implementation of the “Cohen-Daubechies-Feauveau9/7” lifting algorithm on a NVIDIA GPU in order to accelerate the computation with single and double-precision of some test images. Methods/Statistical Analysis: The DWT “Cohen-Daubechies-Feauveau 9/7” filter is implemented on a GPU with MatLab using the in-house parallel computation toolbox (PCT). The performance of both CPU and GPU implantations are compared with single and double precision on some test image. Findings: The investigational results show that the speedup is proportional to the image size until reaching a maximum at 40962 pixels for single-precision and 20482 pixels for double-precision; beyond these values the speedup decreases. The performance with GPU is enhanced, compared to that with CPU, by a factor above 2 for a single-precision of 40962 pixels image size and by a factor above 3 for a double-precision of 20482 pixels image size. By computing the Peak Signal-to-Noise Ratio (PSNR) at critical points after compression, we concluded that with single-precision we can compress larger image sizes without altering the image quality. Applications/Improvements: An efficient parallel implementation of the algorithm on GPU may lead to higher performances. The proposed idea in this work could be also extended to provide high efficiency video coding.

Keywords: Cohen-Daubechies-Feauveau 9/7, DWT (Discrete Wavelet Transformation), JPEG2000, MatLab-GPU, Single and Double Precision