A Multiplication-Free Algorithm and A Parallel Architecture for Affine Transformation

Affine transformation is widely used in image processing. Recently, it is recommended by MPEG-4 for video motion compensation. This paper presents a novel low power parallel architecture for texture warping using affine transformation (AT). The architecture uses a novel multiplication-free algorithm that employs the algebraic properties of the AT. Low power has been achieved at different levels of the design. At the algorithmic level, replacing multiplication operations with bit shifting saves the power and delay of using a multiplier. At the architecture level, low power is achieved by using parallel computational units, where the latency constraints and/or the operating latency can be reduced. At the circuit level, using low power building blocks (such as low power adders) contributes to the power savings. The proposed architecture is used as a computational kernel in video object coders. It is compatible with MPEG-4 and VRML standards. The architecture has been prototyped in 0.6 μm CMOS technology with three layers of metal. The performance of the proposed architecture shows that it can be used in mobile and handheld applications.

[1]  Jie Chen,et al.  Algorithm-based low-power and high-performance multimedia signal processing , 1998, Proc. IEEE.

[2]  F. Rocca,et al.  Interframe Redundancy Reduction of Video Signals Generated by Translating Objects , 1977, IEEE Trans. Commun..

[3]  Michael T. Orchard,et al.  Adaptive entropy constrained transform coding of magnetic resonance image sequences , 1995 .

[4]  Gary J. Sullivan,et al.  Motion compensation for video compression using control grid interpolation , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[5]  B. Liu,et al.  Interslice coding of magnetic resonance images using deformable triangular patches , 1994, Proceedings of 1st International Conference on Image Processing.

[6]  A. Murat Tekalp,et al.  2-D mesh geometry and motion compression for efficient object-based video representation , 1997, Proceedings of International Conference on Image Processing.

[7]  Gerald Holzmann,et al.  Beyond Photography: The Digital Darkroom , 1988 .

[8]  Hiroshi Harashima,et al.  Motion compensation based on spatial transformations , 1994, IEEE Trans. Circuits Syst. Video Technol..

[9]  Michael F. Goodchild,et al.  Geocoding and Geosampling , 1984 .

[10]  George Wolberg,et al.  Digital image warping , 1990 .

[11]  Bilge Gunsel,et al.  Two-dimensional mesh-based visual-object representation for interactive synthetic/natural digital video , 1998, Proc. IEEE.

[12]  Aria Nosratinia,et al.  New kernels for fast mesh-based motion estimation , 2001, IEEE Trans. Circuits Syst. Video Technol..

[13]  Chung-Lin Huang,et al.  A new motion compensation method for image sequence coding using hierarchical grid interpolation , 1994, IEEE Trans. Circuits Syst. Video Technol..

[14]  A. Murat Tekalp,et al.  Tracking Motion and Intensity Variations Using Hierarchical 2-D Mesh Modeling for Synthetic Object Transfiguration , 1996, CVGIP Graph. Model. Image Process..

[15]  Hiroshi Harashima,et al.  Iterative motion estimation method using triangular patches for motion compensation , 1991, Other Conferences.

[16]  Michael T. Orchard,et al.  Interframe coding of magnetic resonance images , 1996, IEEE Trans. Medical Imaging.

[17]  R. Haralick Automatic remote sensor image processing , 1976 .

[18]  H. Brusewitz,et al.  Motion compensation with triangles , 1990 .

[19]  J. Nieweglowski,et al.  A novel video coding scheme based on temporal prediction using digital image warping , 1993, IEEE 1993 International Conference on Consumer Electronics Digest of Technical Papers.

[20]  A. Murat Tekalp,et al.  Object-based video manipulation and composition using 2D meshes in VRML , 1997, Proceedings of First Signal Processing Society Workshop on Multimedia Signal Processing.