Adaptive Optimization Design of Vector Error Diffusion Algorithm and IP Core for FPGA

A vector error diffusion algorithm can obtain better halftone results than a scalar error diffusion algorithm in digital printing, thus, extensive research work about the vector error diffusion has been done to decrease the time that users wait for printing. In this paper, an improved vector error diffusion IP core is proposed. The IP is implemented in FPGA and can meet the requirement of real-time printing by the improvements as follow: three R G B planes are computed in parallel, a matrix-valued error filter is designed to diffuse error among the three planes, matrix-valued pre-stored memory is created to speed multiplications and five stage pipelines are adopted to replace traditional sequential processes. Based on the improvements, we build a practical hardware test system on the SoCKit platform. The test results show that optimal algorithm only needs one clock circle to get the halftone result of a pixel on average and can meet the requirements of practical printing.

[1]  Zeng Ping.  A Vector Error Diffusion Half-toning Algorithm for Color Image , 2005 .

[2]  Yoichi Miyake,et al.  Color digital halftoning taking colorimetric color reproduction into account , 1996, J. Electronic Imaging.

[3]  Brian A. Wandell,et al.  A spatial extension of CIELAB for digital color‐image reproduction , 1997 .

[4]  Daniel L. Lau,et al.  FPGA based parallel architecture implementation of Stacked Error Diffusion algorithm , 2011, 2011 IEEE 9th Symposium on Application Specific Processors (SASP).

[5]  Chongyu Chen,et al.  Structure-Preserving Image Super-Resolution via Contextualized Multitask Learning , 2017, IEEE Transactions on Multimedia.

[6]  Hideaki Kawano,et al.  A Color Quantization Based on Vector Error Diffusion and Particle Swarm Optimization Considering Human Visibility , 2015, PSIVT.

[7]  David J. Sakrison,et al.  The effects of a visual fidelity criterion of the encoding of images , 1974, IEEE Trans. Inf. Theory.

[8]  B. Wandell,et al.  Appearance of colored patterns: pattern-color separability. , 1993, Journal of the Optical Society of America. A, Optics, image science, and vision.

[9]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[10]  Theophano Mitsa,et al.  Evaluation of contrast sensitivity functions for the formulation of quality measures incorporated in halftoning algorithms , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Niranjan Damera-Venkata,et al.  Design and analysis of vector color error diffusion halftoning systems , 2001, IEEE Trans. Image Process..

[12]  Zhigang Fan.  Boundary artifacts reduction in vector error diffusion , 1998, Electronic Imaging.

[13]  Zi Huang,et al.  Effective Multiple Feature Hashing for Large-Scale Near-Duplicate Video Retrieval , 2013, IEEE Transactions on Multimedia.

[14]  Quan Wang,et al.  Parallel design and implementation of Error Diffusion Algorithm and IP core for FPGA , 2015, Multimedia Tools and Applications.

[15]  XiangShiming,et al.  Cross-Modal Hashing via Rank-Order Preserving , 2017 .

[16]  Zheng Lu,et al.  Exploiting Video Quality Information With Lightweight Network Coordination for HTTP-Based Adaptive Video Streaming , 2018, IEEE Transactions on Multimedia.

[17]  Vishal Monga,et al.  Color error-diffusion halftoning , 2003, IEEE Signal Process. Mag..

[18]  Doron Shaked,et al.  Color diffusion: error diffusion for color halftones , 1998, Electronic Imaging.

[19]  V. Monga,et al.  Linear color-separable human visual system models for vector error diffusion halftoning , 2003, IEEE Signal Processing Letters.