Adaptive linear prediction for lossless coding of greyscale images

Ye, H.; Deng, Guang; Devlin, John

doi:10.1109/icip.2000.900911

Cited by 23 publications

(22 citation statements)

References 2 publications

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“…The problem to be solved was the fact, that for the analyzed images the optimal neighborhood (common for all subpredictors) were situated in the whole analyzed range of k, i.e., from 3 to 30. Seemann and Tisher (Seemann & Tisher, 1997a) have proposed k = 3, Deng in his works has used k = 4 (Deng & Ye, 1999, 2003Ye et al, 2000). As a result of our works, for a set of 45 various benchmark images, the best average results have been obtained for k = 10.…”

Section: Subpredictor Blending Methodssupporting

confidence: 55%

“…In Table 1, it is presented a comparison of the bitrates of the method proposed in this paper, Blend-V (the intraframe mode), with a few software-based, but fast and effective methods known from literature: JPEG-LS (Strutz, 2002), HBB (Seemann, et al 1997b), CALIC (Ye et al, 2000), P13 (Deng & Ye, 1999). Among the compared methods there is also a bit slower, but effective method LAT-RLMS (Marusic & Deng, 2002), which shows the high efficiency of the proposed method, which is characterized with the lowest bitrate.…”

Section: Resultsmentioning

confidence: 99%

“…It is the reason why we resigned from the methods for selecting an individual static predictor (or a set of such the predictors) based on the criteria of minimizing a minimum mean square error, MMSE (Sayood, 2002;Wu & Memon, 1996) (which requires introducing delays and solving sets of linear equations using floating numbers), and other methods needed an introductory preparation of modeling and compression parameters. The works of G. Deng (Deng & Ye, 1999, 2003Ye et al, 2000) show high efficiency of the blending prediction method. Having a given set a simple, constant predictors (named further subpredictors), one may conclude that the unconditional entropy (0-order) value obtained by an encoding with each of these subpredictors individually are far from being efficient; however, using even 7 simple subpredictors together leads to significant decrease of the entropy value (Seemann & Tisher, 1997a) in the majority of benchmark images.…”

Section: Subpredictor Blending Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Effective Video Encoding in Lossless and Near-lossless Modes

Ulacha¹

2011

Recent Advances on Video Coding

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Section: Subpredictor Blending Methodssupporting

confidence: 55%

Section: Resultsmentioning

confidence: 99%

Section: Subpredictor Blending Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Effective Video Encoding in Lossless and Near-lossless Modes

Ulacha¹

2011

Recent Advances on Video Coding

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“…T HERE have been great advances in lossless image coding recently [1]- [23], [26]- [31]. Some of which are based on reversible wavelet transformation using lifting structure [6]- [10].…”

Section: Introductionmentioning

confidence: 99%

Least-Squares-Based Switching Structure for Lossless Image Coding

Kau

Lin

2007

IEEE Trans. Circuits Syst. I

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Abstract-Many coding methods are more efficient with some images than others. In particular, run-length coding is very useful for coding areas of little changes. Adaptive predictive coding achieves high coding efficiency for fast changing areas like edges. In this paper, we propose a switching coding scheme that will combine the advantages of both run-length and adaptive linear predictive coding. For pixels in slowly varying areas, run-length coding is used; otherwise least-squares (LS)-adaptive predictive coding is used. Instead of performing LS adaptation in a pixel-by-pixel manner, we adapt the predictor coefficients only when an edge is detected so that the computational complexity can be significantly reduced. For this, we use a simple yet effective edge detector using only causal pixels. This way, the proposed system can look ahead to determine if the coding pixel is around an edge and initiate the LS adaptation in advance to prevent the occurrence of a large prediction error. With the proposed switching structure, very good prediction results can be obtained in both slowly varying areas and pixels around boundaries. Furthermore, only causal pixels are used for estimating the coding pixels in the proposed encoder; no additional side information needs to be transmitted. Extensive experiments as well as comparisons to existing state-of-the-art predictors and coders will be given to demonstrate its usefulness.

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“…Unfortunately a large context like that one would require more training data, but this data might not be available if the image statistics change near i, j, k. [15] …”

Section: The Lm Compression Algorithmmentioning

confidence: 99%

A Block-Based Inter-Band Lossless Hyperspectral Image Compressor

Slyz

Zhang

Data Compression Conference

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Abstract:We propose a hyperspectral image compressor called BH which considers its input image as being partitioned into square blocks, each lying entirely within a particular band, and compresses one such block at a time by using the following steps: first predict the block from the corresponding block in the previous band, then select a predesigned code based on the prediction errors, and finally encode the predictor coefficient and errors. Apart from giving good compression rates and being fast, BH can provide random access to spatial locations in the image.We hypothesize that BH works well because it accommodates the rapidly changing image brightness that often occurs in hyperspectral images. We also propose an intraband compressor called LM which is worse than BH, but whose performance helps explain BH's performance.

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Adaptive linear prediction for lossless coding of greyscale images

Cited by 23 publications

References 2 publications

Effective Video Encoding in Lossless and Near-lossless Modes

Effective Video Encoding in Lossless and Near-lossless Modes

Least-Squares-Based Switching Structure for Lossless Image Coding

A Block-Based Inter-Band Lossless Hyperspectral Image Compressor

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