Adaptive arithmetic coding for image prediction errors

Chaoui

2015

SIViP

In this paper, we proposed a new geometric finite mixture model-based adaptive arithmetic coding (AAC) for lossless image compression. Applying AAC for image compression, large compression gains can be achieved only through the use of sophisticated models that provide more accurate probabilistic descriptions of the image. In this work, we proposed to divide the residual image into nonoverlapping blocks, and then we model the statistics of each block by a mixture of geometric distributions of parameters estimated through the maximum likelihood estimation using the expectation-maximization algorithm. Moreover, a histogram tail truncation method within each predicted error block is used in order to reduce the number of symbols in the arithmetic coding and therefore to reduce the effect of the zero-occurrence symbols. Experimentally, we showed that using convenient block size and number of mixture components in conjunction with the prediction technique median edge detector, the proposed method outperforms the well known lossless image compressors.

Section: Introductionmentioning

confidence: 99%

A finite mixture model of geometric distributions for lossless image compression

Chaoui

2015

SIViP

“…Arithmetic coding (AC) is widely used for efficient entropy coding of data by the most interesting image and video standards such as JBIG2, JPEG2000 and H.264/AVC, and by a variety of lossless data compression methods operating at bit- [1,7], pixel- [3,8,10,24] or predictive error pixel levels [6,12,14,16,23]. AC requires a statistical model to estimate the probabilities of the source symbols.…”

Section: Introductionmentioning

confidence: 99%

“…The probability distributions used in [23] include the most commonly used distributions in image modeling such as Laplacian distribution, Gaussian distribution, t-distribution and generalized Gaussian distribution. Kuroki et al [6] proposed a model that estimates the probability of each error pixel by a Laplacian distribution with zero mean. The compression ratios obtained in [6] are on average 5 % higher than those by conventional AC.…”

Section: Introductionmentioning

confidence: 99%

“…Kuroki et al [6] proposed a model that estimates the probability of each error pixel by a Laplacian distribution with zero mean. The compression ratios obtained in [6] are on average 5 % higher than those by conventional AC. In [12], the probability density functions of prediction errors are categorized by context modeling and modeled by generalized Gaussian functions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Semiparametric Finite Mixture Model-Based Adaptive Arithmetic Coding for Lossless Image Compression

Circuits Syst Signal Process

Puech

2015

International audienceIn this paper, we propose a new approach for block-based lossless image compression by defining a new semiparametric finite mixture model-based adaptive arithmetic coding. Conventional adaptive arithmetic encoders start encoding a sequence of symbols with a uniform distribution, and they update the frequency of each symbol by incrementing its count after it has been encoded. When encoding an image row by row or block by block, conventional adaptive arithmetic encoders provide the same compression results. In addition, images are normally non-stationary signals, which means that different areas in an image have different probability distributions, so conventional adaptive arithmetic encoders which provide probabilities for the whole image are not very efficient. In the proposed compression scheme, an image is divided into non-overlapping blocks of pixels, which are separately encoded with an appropriate statistical model. Hence, instead of starting to encode each block with a uniform distribution, we propose to start with a probability distribution which is modeled by a semiparametric mixture obtained from the distributions of its neighboring blocks. The semiparametric model parameters are estimated through maximum likelihood using the expectation–maximization algorithm in order to maximize the arithmetic coding efficiency. The results of comparative experiments show that we provide significant improvements over conventional adaptive arithmetic encoders and the state-of-the-art lossless image compression standards

“…The AC has been widely used as an efficient entropy coding technique by most interesting image and video standards such as JBIG2, JPEG2000 and H.264/AVC, and by a variety of lossless data compression methods operating in bit- [10,11], pixel- [12][13][14][15] or predictive error pixel-level [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Lossless chaos‐based crypto‐compression scheme for image protection

Puech

2014

IET Image Processing

In this study, the authors proposed a new scheme which performs both lossless compression and encryption of images. Lossless compression is done by arithmetic coding (AC) while encryption is based on a chaos-based pseudorandom bit generator. Hence, they proposed to incorporate recent results of chaos theory into AC in order to shuffle the cumulative frequency vector of input symbols chaotically to make AC secure and the decoding process completely key-dependent. Many other techniques based on varying the statistical model used by AC have been proposed in literature, however, these techniques suffer from losses in compression efficiency that result from changes in entropy model statistics and are weak against known attacks. The proposed compression-encryption techniques were developed and discussed. The numerical simulation analysis indicates that the proposed scheme is highly satisfactory for image encryption without any AC compression efficiency loss. In addition, it can be incorporated into any image compression standard or algorithm employing AC as entropy coding stage, including static, adaptive and context-based adaptive models, and at any level, including bit, pixel and predictive error pixel levels.