L. Ke and M. W. Marcellin, "Near-lossless image compression: minimum-entropy, constrainederror DPCM," IEEE Trans. on Image Processing, vol. 7, no. 2, pp. 225--228, Feb. 1998. Home/Search Document Details and Download
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A Comparative Study of L∞-Distortion Limited Image .. - Hartenstein, Herz, Saupe (1997) (Correct)
....signal is 7.0, after quantization with tolerance = 2, and after prediction the sequence can be coded with an entropy of 3.1. The solution found with the GA only gave an entropy of 3.9. Thus, the GA is not even able to beat the method DPCM1. The minimum entropy constrained error DPCM (MECE) of [5] is another method that tries to minimizes the entropy of the prediction residual sequence. It uses an iterative optimization method that arrives at a local optimum. MECE. Assume that an ideal entropy coder is given for a fixed residual distribution. To find the optimal element of N (s) for ....
....8.6 SPIHT 5.9 4.6 0.8 3.9 1.4 3.4 2.0 3.0 2.5 2.7 3.1 1.9 5.9 1.4 7.2 Table 3: Compression results for the Landsat image. the number of segments [9] However, in this case the global optimum can no longer be achieved by linear programming alone. Instead, an iterative procedure similar to that in [5] can be applied leading to a locally optimal solution. 5. DISTORTION LIMITED VQ The DLVQ method proposed by [12] is a multistage vector quantization approach. The image is partitioned, e.g. in 4 Theta 4 blocks. A block is coded by a predictive vector quantizer. If there is a component with a ....
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Ke, L., Marcellin, M. W., Near-lossless image compression: minimum entropy, constrainederror DPCM, Proceedings of the International Conference on Image Processing (ICIP), Washington, D. C., 1995
Analysis of Trellis Quantization for Near-Lossless Image Coding - Hartenstein, Wu (1998) (Correct)
....the required tolerance. The near lossless version of CALIC [1] uses this mechanism. But since quantization is inside the DPCM loop changing a pixel value at the current position affects subsequent predictions of forthcoming pixels. The trellis quantization scheme proposed by Ke and Marcellin [2] tries to take into account those global implications of quantization. However, despite its high computational cost the TQ scheme is inferior to the near lossless CALIC. In this paper we reexamine the trellis quantization approach for near lossless image coding, and point out its weaknesses that ....
....precision. For example, the near lossless version of CALIC [1] works in that fashion. Trellis quantization is a technique to minimize the bit rate under a given L1 bound. Namely, we replace uniform memoryless scalar quantization of prediction residues by trellis quantization as proposed by [2]. Instead of a simple nearest neighbor quantizer mapping on a pixel by pixel basis, trellis quantization tries to factor in the global impact of individual quantization errors on predictions and on bit rate. In [2] Ke and Marcellin used dynamic programming technique to design a trellis quantizer ....
[Article contains additional citation context not shown here]
L. Ke and M. W. Marcellin, Near-lossless image compression: minimum-entropy, constrained-error DPCM, Proc. IEEE International Conference on Image Processing, Washington, D.C., Oct. 1995.
Region-Based Near-Lossless Image Compression - Armando Pinho Dep (2001) (Correct)
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L. Ke and M. W. Marcellin, "Near-lossless image compression: minimum-entropy, constrainederror DPCM," IEEE Trans. on Image Processing, vol. 7, no. 2, pp. 225--228, Feb. 1998.
Color Transformations for Lossless Image Compression - Domanski, Rakowski (2000) (Correct)
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L. Ke, M.W. Marcellin, "Near-lossless image compression: minimum-entropy, constrained-error DPCM", IEEE Trans. Image Processing, vol. 7, #998, pp. 225-228.
Wavelet-Based Fixed and Embedded.. - Alecu, Munteanu.. (2002) (Correct)
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L. Ke and M. W. Marcellin, "Near-lossless image compression: minimum-entropy, constrained-error DPCM," IEEE Trans. Image Processing, vol. 7, pp. 225228, 1998.
Embedded L-Infinite Constrained Compression Of.. - Alecu, Munteanu.. (2002) (Correct)
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L. Ke and M. W. Marcellin, "Near-lossless image compression: minimum-entropy, constrained-error DPCM," IEEE Trans. Image Processing, vol. 7, pp. 225-228, 1998.
Optimal Piecewise Linear Image Coding - Saupe (1998) (8 citations) (Correct)
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L. Ke and M. W. Marcellin, "Near-lossless image compression: minimum entropy, constrained-error DPCM," Proc. IEEE ICIP'95 , 1995.
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