Lossless Compression of Color Sequences Using Optimal Linear Prediction Theory

Andriani, Stefano; Calvagno, G.

doi:10.1109/tip.2008.2003391

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…It predicts the time series in the future or in the past according to obtained time series. In recent years linear prediction technique has found extensive applications in the fields of voice identification, image processing, and signal frequency estimation [10][11][12]. This paper incorporates spatial linear prediction technique into the ETAM method to propose a new hybrid technique called LP-ETAM method for the bearing estimation of multiple coherent underwater targets.…”

Section: Eurasip Journal On Advances In Signal Processingmentioning

confidence: 99%

Application of Linear Prediction Technique to Passive Synthetic Aperture Processing

Hou

Huang

Jiang

et al. 2010

EURASIP J. Adv. Signal Process.

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A method for the synthesis of an aperture with improved angular resolution and array gain is described. The proposed method explores the merit of linear prediction technique to improve the performance of conventional ETAM (extended towed array measurements) method. Previous efforts to improve the ETAM method generally focused on how to get more accurate estimation of overlap correlator, with an aim to reduce bearing estimation variance. In this paper, however, we discuss how to further improve the angular resolution when the effective synthetic aperture is rather limited. We resort to linear prediction technique to further extend the synthetic aperture obtained by ETAM, which produces a much longer virtual aperture. Results from simulations and lake experiment showed that the proposed LP-ETAM method achieved better angular resolution than ETAM.

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Section: Eurasip Journal On Advances In Signal Processingmentioning

confidence: 99%

Application of Linear Prediction Technique to Passive Synthetic Aperture Processing

Hou

Huang

Jiang

et al. 2010

EURASIP J. Adv. Signal Process.

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“…In order to describe the proposed method, we will briefly discuss the latest international standard for lossless and near lossless still image compression developed by JPEG, JPEG-LS. The main compression techniques proposed in JPEG-LS can be summarised as: (1) run-length coding; (2) nonlinear prediction; (3) context-based statistics modelling; and (4) Golomb-Rice coding [19][20][21].…”

Section: Review Of the Jpeg-lsmentioning

confidence: 99%

Lossless compression of multispectral images using spectral information

Shi

Tang

2009

SPIE Proceedings

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Multispectral images are available for different purposes due to developments in spectral imaging systems. The sizes of multispectral images are enormous. Thus transmission and storage of these volumes of data require huge time and memory resources. That is why compression algorithms must be developed. A salient property of multispectral images is that strong spectral correlation exists throughout almost all bands. This fact is successfully used to predict each band based on the previous bands. We propose to use spectral linear prediction and entropy coding with context modeling for encoding multispectral images. Linear prediction predicts the value for the next sample and computes the difference between predicted value and the original value. This difference is usually small, so it can be encoded with less its than the original value. The technique implies prediction of each image band by involving number of bands along the image spectra. Each pixel is predicted using information provided by pixels in the previous bands in the same spatial position. As done in the JPEG-LS, the proposed coder also represents the mapped residuals by using an adaptive Golomb-Rice code with context modeling. This residual coding is context adaptive, where the context used for the current sample is identified by a context quantization function of the three gradients. Then, context-dependent Golomb-Rice code and bias parameters are estimated sample by sample. The proposed scheme was compared with three algorithms applied to the lossless compression of multispectral images, namely JPEG-LS, Rice coding, and JPEG2000. Simulation tests performed on AVIRIS images have demonstrated that the proposed compression scheme is suitable for multispectral images.

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“…There are also many LS predictor methods for lossless video coding, such as [3] [4], which are based on the pixelbased coding. In LOCO 3-D method [5], an extension of the JPEG-LS scheme to take into account 3-D information is used and the coding algorithm is based on three fundamental blocks similar to those described for JPEG-LS, with the difference that the prediction is built by considering also the pixels from previous frame, not only the current frame.…”

Section: Introductionmentioning

confidence: 99%

Adaptive linear prediction for lossless video coding

Shen

Zhu

2012

2012 IEEE 11th International Conference on Signal Processing

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Linear prediction is an efficient tool for lossless video coding; however data overfitting and context dilution often occur which reduce the prediction efficiency, especially in occlude and large motion image area. In this paper, we will adopt intra and inter two liner prediction modes to eliminate the redundancy among temporal and spatial neighbor pixels. The two prediction modes can be adaptively switched based on their prediction accuracy evaluation in predefined neighborhood region. Experiment results shows that our proposed method can improve prediction efficiency up to 0.27dB.

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Lossless Compression of Color Sequences Using Optimal Linear Prediction Theory

Cited by 6 publications

References 22 publications

Application of Linear Prediction Technique to Passive Synthetic Aperture Processing

Application of Linear Prediction Technique to Passive Synthetic Aperture Processing

Lossless compression of multispectral images using spectral information

Adaptive linear prediction for lossless video coding

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