GPU-based implementation of CABAC for 3-Dimensional Medical Image Compression
Keywords:Context-based Adaptive Binary Arithmetic Coder, Discrete Wavelet Transform, Graphical Processing Unit, Compression Ratio, Peak Signal to Noise Ratio,
AbstractContext-based Adaptive Binary Arithmetic Coder (CABAC) is the advanced entropy coding tool employed by main and higher profiles of H.264/AVC. In these applications, hardware acceleration is needed as the computational load of CABAC is high. To improve the implementation time, Graphical Processing Unit (GPU) NVIDIA GeForce 820M has been used. This paper describes the design and GPU implementation of CABAC and comparative study of Discrete Wavelet Transform (DWT) and without DWT for threedimensional (3-D) medical image compression systems. The proposed system architectures were simulated in MATLAB. Implementation results on Magnetic Resonance Image (MRI) and Computed Tomography (CT) images with GPU and Central Processing Unit (CPU) are presented, showing GPU significantly outperformed with respect to a single-threaded CPU implementation. These results revealed that GPU is the best candidate for image compression application. In overall, CT and MRI modalities with DWT outperform in term of compression ratio, Peak Signal to Noise Ratio (PSNR) and latency compared with images for CT and MRI without DWT process.
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