GPU-based implementation of CABAC for 3-Dimensional Medical Image Compression


  • Afandi Ahmad Department of Computer Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), Johor, 86400, Malaysia. Reconfigurable Computing for Analytic Acceleration Focus Group (ReCAA), Microelectronics and Nanotechnology – Shamsuddin Research Centre (MiNT-SRC), Universiti Tun Hussein Onn Malaysia (UTHM), Johor, 86400, Malaysia
  • Azlan Muharam Kolej Komuniti Masjid Tanah, Kementerian Pendidikan Tinggi, Paya Rumput, 78300 Masjid Tanah, Melaka, Malaysia. Reconfigurable Computing for Analytic Acceleration Focus Group (ReCAA), Microelectronics and Nanotechnology – Shamsuddin Research Centre (MiNT-SRC), Universiti Tun Hussein Onn Malaysia (UTHM), Johor, 86400, Malaysia
  • Abbes Amira Department of Computer Science and Engineering, Qatar University, P. O. Box 2713, Doha, Qatar.


Context-based Adaptive Binary Arithmetic Coder, Discrete Wavelet Transform, Graphical Processing Unit, Compression Ratio, Peak Signal to Noise Ratio,


Context-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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How to Cite

Ahmad, A., Muharam, A., & Amira, A. (2017). GPU-based implementation of CABAC for 3-Dimensional Medical Image Compression. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-8), 45–50. Retrieved from