Paul Heckbert
Abstract
Algorithms for adaptive, tapered quantization of color images are described. The research is motivated by the desire to display high-quality reproductions of color images with small frame buffers. It is demonstrated that many color images which would normally require a frame buffer having 15 bits per pixel can be quantized to 8 or fewer bits per pixel with little subjective degradation. In most cases, the resulting images look significantly better than those made with uniform quantization.
The color image quantization task is broken into four phases:
1) Sampling the original image for color statistics
2) Choosing a colormap based on the color statistics
3) Mapping original colors to their nearest neighbors in the colormap
4) Quantizing and redrawing the original image (with optional dither).
Several algorithms for each of phases 2-4 are described, and images created by each given.
- 1 Bellman, R. Dynamic Programming. Princeton University Press, Princeton, 1957. Google Scholar
Digital Library
- 2 Bentley, J. L., Friedman, J. H. Data structures for range searching. Computing Surveys 11, 4 (Dec. 1979), 397-409. Google ScholarDigital Library
- 3 Bruce, J. D. Optimum Quantization. MIT R.L.E. Technical Report #429, (1965).Google Scholar
- 4 Elias, P. Bounds on performance of optimum quantizers. IEEE Trans. on Information Theory IT-16, 2 (Mar. 1970) 172-184.Google ScholarDigital Library
- 5 Floyd, R. W., Steinberg, L. An adaptive algorithm for spatial gray scale. SID 75, Int. Symp. Dig. Tech. Papers (1975), 36.Google Scholar
- 6 Friedman, J. J., Bentley, J. L., and Finkel, R. A. An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Software 3, (Sept. 1977), 209-226. Google ScholarDigital Library
- 7 Gray, R. M., Kieffer, J. C., and Linde, Y. Locally optimal block quantizer design. Information and Control 45 (1980) 178-198.Google ScholarCross Ref
- 8 Heckbert, P. Color Image Quantization for Frame Buffer Display. B.S. thesis Architecture Machine Group, MIT, Cambridge, Mass., 1980.Google Scholar
- 9 Huang, T. S., Tretiak, O. J., Prasada, B. T., and Yamaguchi, Y. Design considerations in PCM transmission of low-resolution monochrome still pictures. Proc. IEEE 55, 3 (Mar. 1967), 331.Google ScholarCross Ref
- 10 In der Smitten, F. J. Data-reducing source encoding of color picture signals based on chromaticity classes. Nachrichtentech. Z. 27, (1974), 176.Google Scholar
- 11 Jain, A. K., and Pratt, W. K. Color image quantization. National Tele-communications Conference 1972 Record, IEEE Pub. No. 72, CHO 601-5-NTC, (Dec. 1972).Google Scholar
- 12 Jarvis, J. F., Judice, N., and Ninke, W.H. A survey of techniques for the display of continuous tone pictures on bilevel displays. Computer Graphics and Image Processing 5, 1 (Mar. 1976), 13-40.Google ScholarCross Ref
- 13 Knuth, D. E. The Art of Computer Programming, vol. 3, Sorting and Searching. Addison-Wesley, Reading, Mass., 1973. Google ScholarDigital Library
- 14 Koontz, W. L. G., Narendra, P. M., and Fukunaga, K. A branch and bound clustering algorithm. IEEE Trans. Comput. C-24, 9 (Sept. 1975), 908-915.Google ScholarDigital Library
- 15 Limb, J. O., Rubinstein, C. B., and Thompson, J. E. Digital coding of color video signals - a review. IEEE Trans. Commun. COM-25, 11 (Nov. 1977), 1349-1385.Google ScholarCross Ref
- 16 Lloyd, S. P. Least squares quantization in PCM's. Bell Telephone Labs Memo, Murray Hill, N.J., 1957.Google Scholar
- 17 Max, J. Quantizing for minimum distortion. IRE Trans. Information Theory IT-6, (Mar. 1960), 7.Google ScholarCross Ref
- 18 Newman, W. M., and Sproull, R. F. Principles of Interactive Computer Graphics. MacGraw-Hill, New York, 1979. Google ScholarDigital Library
- 19 Panter, P. F., and Dite, W. Quantization distortion in pulse-count modulation with nonuniform spacing of levels. Proc. IRE 39, 1 (Jan. 1951), 44.Google ScholarCross Ref
- 20 Pratt, W. K. Digital Image Processing. John Wiley and Sons, New York, 1978. Google ScholarDigital Library
- 21 Roberts, L. G. Picture coding using pseudo-random noise. IRE Trans. Information Theory IT-8, (Feb. 1962), 145.Google ScholarCross Ref
- 22 Stenger, L. Quantization of TV chrominance signals considering the visibility of small color differences. IEEE Trans. Communications COM-25, 11 (Nov. 1977), 1393.Google ScholarCross Ref
Index Terms
- Color image quantization for frame buffer display
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