&lt;title&gt;Context-dependent distribution shaping and parameterization for lossless image compression&lt;/title&gt;

Langdon, Glen G.; Haidinyak, C.

doi:10.1117/12.186574

Cited by 6 publications

(3 citation statements)

References 8 publications

(17 reference statements)

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“…For the compression results reported by this paper, we used the following linear predictor in the third pass. : (6) Clearly, progressive, multiresolution decompression is a natural by-product of the above three-pass encoding scheme. Successive passes of an image reconstruct it at increasing resolutions/precisions and bit rates.…”

Section: Three-pass Interlaced Predictive Coding Schemementioning

confidence: 99%

“…The theoretical framework of lossless image coding has been and seemingly will continue to be predictive coding based on adaptive, statistical context modeling of images. Pixel values are entropy coded using estimated probabilities conditioned on the contexts or states in which the pixels are observed 2, 3,6,10,13,15,16]. Some sets of previously coded pixels that are used in the modeling or prediction of the next unknown pixels are called modeling or prediction contexts of the unknown pixels.…”

Section: Introductionmentioning

confidence: 99%

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Lossless compression of continuous-tone images via context selection, quantization, and modeling

1997

IEEE Trans. on Image Process.

206

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Context modeling is an extensively studied paradigm for lossless compression of continuous-tone images. However, without careful algorithm design, high-order Markovian modeling of continuous-tone images is too expensive in both computational time and space to be practical. Furthermore, the exponential growth of the number of modeling states in the order of a Markov model can quickly lead to the problem of context dilution; that is, an image may not have enough samples for good estimates of conditional probabilities associated with the modeling states. New techniques for context modeling of DPCM errors are introduced that can exploit context-dependent DPCM error structures to the benefit of compression. New algorithmic techniques of forming and quantizing modeling contexts are also developed to alleviate the problem of context dilution and reduce both time and space complexities. By innovative formation, quantization, and use of modeling contexts, the proposed lossless image coder has a highly competitive compression performance and yet remains practical.

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Section: Three-pass Interlaced Predictive Coding Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lossless compression of continuous-tone images via context selection, quantization, and modeling

1997

IEEE Trans. on Image Process.

206

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“…A good error modeling should capture the local characteristics of the image and behavior of the predictor so that the probability estimations formed are highly compressible. The final entropy of the error-residual values conditioned on different context (previously encoded pixels in a certain template structure) proves to be less than the first order entropy of the direct residual error [37,28]. The compression performance can be enhanced if the residual values are classified into various exponential distributions based on their variance calculated using the error values in the context [38].…”

Section: Problem Of Computationmentioning

confidence: 99%

Adaptive edge-based prediction for lossless image compression

Rahul¹

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ADAPTIVE EDGE-BASED PREDICTION FOR LOSSLESS IMAGE COMPRESSION by Rahul G. Parthe The increasing use of images in a variety of applications has heightened the need for methods to compress an image. Image compression is needed in efficient management of the two main resources in computing systems; space and communication time. Computer based data compression has been practiced for decades and image compression has been studied since the 70's. Thus compression algorithms are slowly approaching their theoretical limit. Image data modeling in particular, is very difficult due to the higher order correlations that exist in natural continuous images. Though the established compression results are hard to surpass, there are some aspects that can be considered to achieve better performance. The gain might appear small, but still significant, especially when considered from the viewpoint of increasing image size of available images. Prediction based image compression is generally a two-phase process: the first phase is the prediction process/method, and the second is the error modeling and encoding. Prediction is done based on the previously encoded data [15], which gives information about the local characteristics of the image area using some non-adaptive fixed rule set. The simplest technique used as a predictor is the Differential Pulse Code Modulation (DPCM) [16], which assumes the prediction for the current pixel to be the value of the previous pixel in raster scan. The error generated for encoding is thus calculated using the difference Image Adaptivity: It is important to first identify and classify the image areas into edge and non-edge areas, so that each can be treated differently [31]. Once the pixels are designated and separated into two classes, a predictor suitable for the class and local image characteristics can be applied. An approximate model of the activity around the pixel to be predicted can be adaptively generated and used to better tune the predictor for the current

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An algorithmic study on lossless image compression

Proceedings of Data Compression Conference - DCC '96

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<title>Context-dependent distribution shaping and parameterization for lossless image compression</title>

Cited by 6 publications

References 8 publications

Lossless compression of continuous-tone images via context selection, quantization, and modeling

Lossless compression of continuous-tone images via context selection, quantization, and modeling

Adaptive edge-based prediction for lossless image compression

An algorithmic study on lossless image compression

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