Adaptive Golomb Code for Joint Geometrically Distributed Data and Its Application in Image Coding

Ding, Jian-Jiun; Chen, Hsin-Hui; Wei, Weiyi

doi:10.1109/tcsvt.2012.2211952

Cited by 28 publications

(13 citation statements)

References 14 publications

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“…Also strategies for adapting the Golomb Rice parameter for correlated sources have been presented in [10]. The use of contexts has been proposed for Golomb codes optimization in [11]. In [12] the authors have proposed to use the LPC spectral envelope for the adaptation of the Golomb Rice parameter at each frequency bin for a frequency domain audio encoder.…”

Section: Generic Adaptive Golomb Rice Codingmentioning

confidence: 99%

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Order adaptive Golomb rice coding for high variability sources

Vasilache

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper presents a new perspective on the adaptive Golomb Rice codes that is especially suitable for sources having a highly variable distribution in time. Instead of adapting the Golomb Rice parameter, the encoder adapts the order of the symbols based on a count of occurrences measure. The proposed order adaptive Golomb Rice method is compared against different versions of adaptive arithmetic encoder at the encoding of real audio data stereo parameters. The proposed method shows very fast adaptability in the presence of rapidly changing data with respect to the initial data statistics.

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Section: Generic Adaptive Golomb Rice Codingmentioning

confidence: 99%

“…The artificial counts vector is generated such that its corresponding mapping gives an almost symmetric distribution with the maximum in the centre. The articifial counts vector is given by: h = [1, 3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,30,28,26,24,22,20,18,16,14,12,10,8,6,4,2].…”

Section: A Coding Efficiencymentioning

confidence: 99%

Order adaptive Golomb rice coding for high variability sources

Vasilache

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…The table of the scan order was designed for each prediction mode according to the variance of the Laplacian distribution To evaluate the effectiveness of the proposed LPBAS scheme, two DCT block-based image coding systems (i.e., baseline JPEG and H.264-intra JPEG) are used. Since the two coding systems are inherently different, e.g., the latter one uses 9 intra prediction modes and applies the DCT in the prediction residue domain [10][11][12], while the former one performs the DCT on spatial domain without any block prediction [8], our proposed algorithm is separately designed for them. The details are described in Section 3 and Section 4, respectively.…”

Section: Overview Of Related Workmentioning

confidence: 99%

“…Different from [6], which performed "offline learning" by using all the blocks in the image to generate the scan order patterns, the proposed scheme apply "online learning" to update the scan order pattern only by using the causal blocks and can be run in real time. The predicted variance of the (m, n) th nonzero coefficient of the current block, denoted by V G (u, v) (m, n), can be calculated from (9), and the corresponding probability, denoted by P G (u, v) (m, n), is determined by (10). (10) where c (i, j) (m, n) = 1, if B (i, j) (m, n) ≠ 0, c (i, j) (m, n) = 0 otherwise.…”

Section: Global Variance Prediction and Probability Estimationmentioning

confidence: 99%

“…In this paper, we propose an efficient scanning scheme, which is called local prediction based adaptive scanning (LPBAS), for DCT block-based image compression (i.e., JPEG and the H.264/AVC intra coding based on JPEG [10][11][12]). The main algorithm of the proposed scheme is using the local neighboring DCT block data to predict the variance of each coefficient of the current block to be coded, followed by sorting these variances to generate the scan order pattern.…”

Section: Introductionmentioning

confidence: 99%

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Local prediction based adaptive scanning for JPEG and H.264/AVC intra coding

Chen

Huang

Ding

2013

2013 IEEE International Conference on Image Processing

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In this paper, a new adaptive scanning scheme, which is called local prediction based adaptive scanning (LPBAS), is proposed for discrete cosine transform (DCT) based image compression techniques including JPEG and H.264/AVC intra coding. The conventional zigzag scan order is widely used in image and video coding standards, but it ignores the statistical properties of DCT blocks and has limited performance. In this paper, the LPBAS scheme is proposed to achieve the entropy coding gain, where the scan order patterns are adaptively generated and updated based on the statistics of local neighboring DCT blocks. The proposed scheme improves the efficiency of the two image coding systems, JPEG and the H.264/AVC intra coding system. Simulation results showed that the proposed scheme indeed outperforms the zigzag scanning method and other existing adaptive scanning methods.

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Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach

Perumal

Murugan

2015

Int J Imaging Syst Tech

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Image compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. Image compression techniques are widely used in many applications especially, medical field. Large amount of medical image sequences are available in various hospitals and medical organizations. Large images can be compressed into smaller size images, so that the memory occupation of the image is considerably reduced. Image compression techniques are used to reduce the number of pixels in the input image, which is also used to reduce the broadcast and transmission cost in efficient form. This is capable by compressing different types of medical images giving better compression ratio (CR), low mean square error (MSE), bits per pixel (BPP), high peak signal to noise ratio (PSNR), input image memory size and size of the compressed image, minimum memory requirement and computational time. The pixels and the other contents of the images are less variant during the compression process. This work outlines the different compression methods such as Huffman, fractal, neural network back propagation (NNBP) and neural network radial basis function (NNRBF) applied to medical images such as MR and CT images. Experimental results show that the NNRBF technique achieves a higher CR, BPP and PSNR, with less MSE on CT and MR images when compared with Huffman, fractal and NNBP techniques.

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Adaptive Golomb Code for Joint Geometrically Distributed Data and Its Application in Image Coding

Cited by 28 publications

References 14 publications

Order adaptive Golomb rice coding for high variability sources

Order adaptive Golomb rice coding for high variability sources

Local prediction based adaptive scanning for JPEG and H.264/AVC intra coding

Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach

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