We also analyze a special case of encoding incompressible data. In the paper we analyze effects of using both the GR and the limited codeword length GR codes for encoding actual images, where the set of encoded symbols is finite and the probability distribution is not exactly exponential. The modified GR family of limited codeword length is used in the JPEG-LS algorithm recently included in the DICOM standard. The GR family is used in predictive lossless image compression algorithms since the probability distribution of symbols encoded by those algorithms for typical images is close to exponential.
In the paper we analyze the Golomb-Rice (GR) family of codes, infinite family of prefix codes optimal for encoding symbols of exponential probability distribution. In applications, the results of compressing non-typical data should be known and the data expansion, unavoidable while processing incompressible data, should be minimized by the rule: primum non nocere. For a compression algorithm the worst case image data results in data expansion. Image compression researchers usually focus on results obtained for typical data, however for real life systems results for non-typical data should also be considered. Lossless algorithms are especially important for systems transmitting and archiving medical data, because on the one hand lossy compression of medical images used for diagnostic purposes is forbidden by law, on the other hand the number and sizes of medical images stored or transmitted grows rapidly. Lossless image compression algorithms are used for images that are documents, when lossy compression is not accepted by users, or when we have no knowledge whether lossy compression is allowed or not. We verify this with experiments on a dataset consisting of different categories of images. In summary, our theoretical results show that a combination of Hilbert space-filling curves and Move-To-Front encoding has advantage over other approaches.
#Best simle image compression software update
We also use a result by Angelopoulos and Schweitzer to select Move-To-Front as the best list update algorithm for encoding the linearised sequence. , we establish the advantage of Hilbert space-filling curves over other linearisation techniques such as row-major or column-major curves for preserving the locality during the linearisation. Using a natural model of locality for images introduced by Albers et al. We consider a few approaches, and in particular Hilbert space-filling curves, for linearising the image. In both linearisation and encoding stages, we exploit the locality present in the images to achieve encodings that are as compressed as possible. Given an image represented with a matrix of pixel values, we consider different approaches for linearising the image into a sequence and then encoding the sequence using the Move-To-Front list update algorithm. We consider lossless image compression using a technique similar to bZip2 for sequential data. In particular, the compression algorithm using the new 2-D bi-level block coding scheme achieves the highest CR. The compression algorithm with bi-level coding achieves the highest PSNR when the bit-error rate (BER) is larger than 0.001 and maintains an acceptable PSNR for BER less than 0.001. The performances are validated in terms of compression ratio (CR) and peak signal to noise ratio (PSNR).
#Best simle image compression software code
Finally, the key parameters such as color transformation information, predictor parameters and residue coding parameters are protected using (7, 4) Hamming code in the bit stream before transmission. Comparing with the existing residue coding methods, including 1-D bi-level block coding, interval Huffman coding, and standard Huffman coding, the 2-D bi-level block coding algorithm can improve image compression ratio as well as preserving the bit-error resilience. Third, a new 2-D bi-level block coding is developed to further encode the residue sequences. In order to achieve a better image compression ratio, a particle swarm optimization (PSO) algorithm is adopted to search the best combination from the candidates of color transformations and predictors which generate the minimum residue entropy. Next, predictors in the YCrCb color space are applied to generate the residue sequences. First, a color image in the RGB color space is converted to the YCrCb color space by lossless reversible transformations. The proposed method contains three stages. In this paper, we develop a new color image lossless compression algorithm with bit-error awareness based on a general bi-level block coding method.
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