Abstract
A common method for selecting the best prediction mode based on block matching algorithm is to compare, for each source block, the associated distortions among the available prediction candidates. The human visual perception is sensitive to luminance contrast rather than absolute luminance values. In fact, the human eyes ability to detect the magnitude difference between an object and its background depends on the background luminance average value. The Perceptually Weighted Distortion (PWD) is a new distortion measure that can produce better image quality. In this paper, we propose to add a new feature to the PWD by introducing another diagonal component that yields to a significant quality improvement. The enhanced PWD metric actually outperforms the original PWD and the SAD metric, according to the experimental results, especially in the aspect of reducing block artifacts. An increase in terms of implementation complexity will be noticed as a result of this contribution. Therefore, optimized implementation of the Enhanced PWD exploiting the C64 DSP-Core assets will be presented. In fact, Standard Assembly (SA) is used to implement the different Enhanced PWD functions in order to exploit efficiently the C64 internal architecture and resources. Experimental results show more than 85% improvement in terms of cycle cost compared to C code.
Similar content being viewed by others
References
Thomas, W. (2002). “Study of Final Committee Draft of Joint Video Specification”, ITU-T Rec. H.264 | ISO/IEC 14496–10 AVC, Draft 1.
Vanghn Iverson, Jeff Mc Veigh, Bob Reese, (2004). “Real-Time H.264/AVC Codec On Intel Architectures” (pp. 757–760). International Conference on Image Processing ICIP.
Castagno, R, & Ramponi, G. “A Rational Filter for the Removal of Blocking Artifacts in Image Sequences Coded at Low bitrate”, Proc. Eighth European Signal Processing Conf., EUSIPCO-96, Trieste, Italy, Sept. 10–13, 1996.
Liu, S., & Bovik, A. C. (2002). “Efficient DCT-Domain Blind Measurement and Reduction of Blocking Artifacts”. IEEE Transactions on Circuits and Systems for Video Technology, 12(12), 1139–1149.
Paek, H., Kim, R., & Lee, S. (2000). “A DCT-Based Spatially Adaptive Post-Processing Technique to Reduce the Blocking Artifacts in Transform Coded Images”. IEEE Transactions on Circuits and Systems for Video Technology, 10(1), 36–41.
Bailey, D., Carli, M., Farias, M., & Mitra, S. “Quality Assessment for Block-Based Comprossed Images and Videos with regard to Blockiness Artifacts”, Tyrrhenian International Workshop on Digital Communications (IWDC 2002).
Wee-Chung Liew, A., Yan, H., & Law, N.-F. (2005). “POCS-based Blocking Artifacts Suppression using a Smoothness Constraint Set with Explicit Region Modeling”. IEEE Transactions on Circuits and Systems for Video Technology, 15(6), 795–800.
Meier, T., Ngan, K. N., & Crebbin, G. (1999). “Reduction of Blocking Artifacts in Image and Video Coding”. IEEE Transactions on Circuits and Systems for Video Technology, 9, 3.
Segall, C. A., & Katsaggelos, A. K. “Pre-and Post-processing Algorithms for Compressed Video Enhancement”.
Ben Amara, F., Jerbi, A., Au, J. & Kossentini, F. (2004).“Trailing Artifact Avoidance for Low Bit-rate Block-Based Video Coder” First International Symposium on Control, Communications and Signal Processing, (pp21–24). Hammamet, Tunisia.
Jaehan, I., Ali, J., & Foued, B. A. (2004). “Perceptually weighted distortion measure in low bitrate block-based video coders”. International Conference on Image Processing, 1, 477–479.
Jayant, N. (1992). Signal compression: technology targets and research directions. IEEE Journal on Selected Areas in Communications, 10, 314–323.
Jayant, N., Johnston, J., & Safranek, R. (1993). Signal compression based on model of human perception. Proceedings of the IEEE, 81, 1385–1422.
Chou, C. H., & Li, Y. C. (1995). A perceptually tuned subband image coder based on the measure of Just-Noticeable-Distortion profile. IEEE Transactions on Circuits and Systems for Video Technology, 5(6), 467–476.
Safranek, R. J., & Johnston, J. D. (1989). “A perceptually tuned subband image coder with image dependent quantization and post-quantizatioin data comperssion”. IEEE International Conference on Acoustics, Speech and Signal Processing, 3, 1945–1948.
Lubin, J. (1995). “A visual discrimination model for imaging system design and evaluation”, E. Peli (ed.), Vision Models for Target Detection and Recognition. Chapter 10, (pp. 245–283). World Scientific Publishing Co. Pte. Ltd.
Delaigle, J. F., Devleeschouwer, C., Macq, B., & Langendijk, I. (2002). Human visual system features enabling watermarking. In Proc IEEE International Conference on Multimedia and Expo, 2, 26–29.
Gu, J. (1999). 3D Wavelet-Based Video Codec with Human Perceptual Model. Center for Satellite and Hybrid Communication Networks: Master Thesis.
Takeuchi, T., De Valois, K. K., & Hardy, J. L. (2003). The influence of color on the perception of luminance motion. Vision Research, 43(10), 1159–1175.
Netravali, A. N., & Haskell, B. G. (1988). Digital Pictures: Representation and Compression. New York: Plenum.
Olzak, L. A.. & Thomas, J.P. (1986) Handbook of perception and human performance: seeing spatial patterns, volume 1, chapter 7, (pages 1–56). Wiley, J, New York, University of California, Los Angeles, California.
http://www.compression.ru/video/quality_measure/info_en.html
http://www.compression.ru/video/quality_measure/video_measurement_tool_en.html
Wiegand, T., Sullivan, G. J., Bjøntegaard, G., & Luthra, A. (2003). Overview of the H.264/AVC Video Coding Standard. IEEE Transactions on Circuits and Systems for Video Technology, 13(7), 560–576.
Joch, A., & Kossentini, F. (2002). Demonstration of a Computation-Optimized JVT Codec, Doc. JVT-C148, Joint Video Team (JVT) of ISO/IEC MPEG & ITU-T VCEG (ISO/IEC JTC1/SC29/WG11 and ITU-T SG16 Q.6) (pp. 6–10). Virginia: Fairfax.
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). “Image quality assessment: From error visibility to structural similarity”. IEEE Transactions on Image Processing, 13, 4.
Wang, Z, Bovik, A. C, & Simoncelli, E. P. “Structural approaches to image quality assessment”, Handbook of Image and Video Processing (Al Bovik, eds.), second edition, May 2005.
Wang, Z., Bovik, A. C., & Simoncelli, E. P. “Structural approaches to image quality assessment,” To appear in Handbook of Image and Video Processing (Al Bovik, eds.), second edition, Academic Press, May 2005.
Texas Instruments, “TMS320C6000 CPU and Instruction Set Reference Guide”, spru189f.pdf, 2000.
Texas Instruments, “TMS320DM642 Video/Imaging Fixed-Point Digital Signal Processor”, sprs200a.pdf, 2002.
Lee, E. A., Messerschmitt, D. “Synchronous data flow”, Proceedings of the IEEE, Vol. 75, no.9, Sept. 1987.
Fröhlich, S., & Wess, B. “Optimizing complex machine instructions with dynamic trellis diagrams”, 11th Int. Conf. on Signal Processing Applications & Technology, Dallas, October 2000.
B. Wess., “Optimizing signal flow graph compilers for digital signal processors”, 5th Int. Conf. on Signal Processing Applications & Technology, volume 1, pages 665–670, Dallas, October 1994.
B. Wess, W. Kreuzer, and M. Gotschlich., “Automatic generation of optimized DSP assembly code” Int. Conf. on Industrial Electronics, Control, and Instrumentation, volume 2, pages 979–984, Orlando, November 1995.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Samet, A., Hachicha, A., Ayed, M.A.B. et al. Implementation and Optimization of an Enhanced PWD Metric for H.264/AVC on a TMS320C64 DSP. J Sign Process Syst 69, 143–159 (2012). https://doi.org/10.1007/s11265-011-0641-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11265-011-0641-7