Recently, with the rapid development of cloud storage, secure cloud computing and privacy protection have attracted widespread attention. Reversible data hiding in encrypted images (RDHEI) plays an important role in i...
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Recently, with the rapid development of cloud storage, secure cloud computing and privacy protection have attracted widespread attention. Reversible data hiding in encrypted images (RDHEI) plays an important role in it and has been paid more and more attention. In this paper, an improved RHDEI is proposed. By combining adaptive arithmetic coding and static Huffman coding, the image bit-plane is effectively compressed and a lot of space is made for data embedding. The security of image and embedded data is guaranteed by XOR-encryption and scrambling encryption. The data extractor can embed and extract data without decrypting the carrier image to protect the privacy of the image owner. Experimental results show that the scheme can achieve an average embedding capacity (EC) of up to 3bpp while ensuring lossless recovery of carrier images and correct extraction of embedded data. Compared with state-of-the-art RDHEI methods, our scheme achieves higher EC and better security.
Binary image is useful in our life. For instance, text, line art, halftone image, tax etc. could use this method, so lossless binary image compression is useful for improve this domain. We found that angle freeman cha...
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ISBN:
(纸本)9781728193960
Binary image is useful in our life. For instance, text, line art, halftone image, tax etc. could use this method, so lossless binary image compression is useful for improve this domain. We found that angle freeman chain code for eight connectivity (AF8) is effective in lossless binary image compression. Therefore, we use improved-adaptive-arithmetic-coding to encode character of AF8, and we also decrease character with global and local frequency table thanks to some characteristics of AF8 we found. Then, in experimental result, we show our proposed method is better than AF8 with static arithmeticcoding (SAC), and we also show that the context modeling method we choose is better than the compression coding without context modeling. Furthermore, our method is also better than other method like the ZD code and the AAF8 code.
Context-based adaptive arithmetic coding (CAAC) has high coding efficiency and is adopted by the majority of advanced compression algorithms. In this paper, five new techniques are proposed to further improve the perf...
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Context-based adaptive arithmetic coding (CAAC) has high coding efficiency and is adopted by the majority of advanced compression algorithms. In this paper, five new techniques are proposed to further improve the performance of CAAC. They make the frequency table (the table used to estimate the probability distribution of data according to the past input) of CAAC converge to the true probability distribution rapidly and hence improve the coding efficiency. Instead of varying only one entry of the frequency table, the proposed range-adjusting scheme adjusts the entries near to the current input value together. With the proposed mutual-learning scheme, the frequency tables of the contexts highly correlated to the current context are also adjusted. The proposed increasingly adjusting step scheme applies a greater adjusting step for recent data. The proposed adaptive initialization scheme uses a proper model to initialize the frequency table. Moreover, a local frequency table is generated according to local information. We perform several simulations on edge-directed prediction-based lossless image compression, coefficient encoding in JPEG, bit plane coding in JPEG 2000, and motion vector residue coding in video compression. All simulations confirm that the proposed techniques can reduce the bit rate and are beneficial for data compression.
arithmeticcoding is used in most media compression methods. Context modeling is usually done through frequency counting and look-up tables (LUTs). For long-memory signals, probability modeling with large context size...
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arithmeticcoding is used in most media compression methods. Context modeling is usually done through frequency counting and look-up tables (LUTs). For long-memory signals, probability modeling with large context sizes is often infeasible. Recently, neural networks have been used to model probabilities of large contexts in order to drive arithmetic coders. These neural networks have been trained offline. We introduce an online method for training a perceptron-based context-adaptivearithmetic coder on-the-fly, called adaptive perceptron coding, which continuously learns the context probabilities and quickly converges to the signal statistics. We test adaptive perceptron coding over a binary image database, with results always exceeding the performance of LUT-based methods for large context sizes and of recurrent neural networks. We also compare the method to a version requiring offline training, which leads to equally satisfactory results.
This paper presents a novel lossless compression technique of the context-based adaptive arithmetic coding which can be used to further compress the quantized parameters in audio codec. The key feature of the new tech...
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This paper presents a novel lossless compression technique of the context-based adaptive arithmetic coding which can be used to further compress the quantized parameters in audio codec. The key feature of the new technique is the combination of the context model in time domain and frequency domain which is called time-frequency context model. It is used for the lossless compression of audio coding parameters such as the quantized modified discrete cosine transform (MDCT) coefficients and the frequency band gains in ITU-T G.719 audio codec. With the proposed adaptive arithmetic coding, a high degree of adaptation and redundancy reduction can be achieved. In addition, an efficient variable rate algorithm is employed, which is designed based on both the baseline entropy coding method of G.719 and the proposed adaptive arithmetic coding technique. Experiments show that the proposed technique is of higher efficiency compared with the conventional Huffman coding and the common adaptive arithmetic coding when used in the lossless compression of audio coding parameters. For a set of audio samples used in the G.719 application, the proposed technique achieves an average bit rate saving of 7.2% at low bit rate coding mode while producing audio quality equal to that of the original G.719.
This paper presents a new lossless image compression method based on the learning of pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and contexts obtained by MLPs are forwarded t...
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This paper presents a new lossless image compression method based on the learning of pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and contexts obtained by MLPs are forwarded to adaptivearithmetic encoders, like the conventional lossless compression schemes. The MLP-based prediction has long been attempted for lossless compression, and recently convolutional neural networks (CNNs) are also adopted for the lossy/lossless coding. While the existing MLP-based lossless compression schemes focused only on accurate pixel prediction, we jointly predict the pixel values and contexts. We also adopt and design channel-wise progressive learning, residual learning, and duplex network in this MLP-based framework, which leads to improved coding gain compared to the conventional methods. Experiments show that the proposed method performs better than the conventional non-learning algorithms and also recent learning-based compression methods with practical computation time.
In this work, an efficient and robust learning-based JPEG2000 architecture is proposed. It uses machine learning techniques for predicting and encoding the decision bit in the embedded block coding with optimized trun...
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ISBN:
(纸本)9781728180687
In this work, an efficient and robust learning-based JPEG2000 architecture is proposed. It uses machine learning techniques for predicting and encoding the decision bit in the embedded block coding with optimized truncation (EBCOT) process. First, we apply non-locally weighted ridge regression to predict the quantized wavelet coefficients in the LL subband. Then, during the EBCOT process, we perform inter/intra subband prediction and inter/intra bit plane symbol prediction to estimate the activity of the decision bit using the deep learning architecture. Then, the binary prediction result is treated as an additional context and the decision bit is eventually coded using an advanced context-based adaptive binary arithmetic coder. Simulations show that the proposed framework provides the same visual quality as conventional codecs with as much as 30% bitrate savings.
The paper presents Improved adaptive arithmetic coding algorithm for application in forthcoming HEVC video compression technology. The proposed solution is based on standard CABAC algorithm and uses author's new m...
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ISBN:
(纸本)9783642335648;9783642335631
The paper presents Improved adaptive arithmetic coding algorithm for application in forthcoming HEVC video compression technology. The proposed solution is based on standard CABAC algorithm and uses author's new mechanism of data statistics modeling that is based on CTW technique. The improved CABAC algorithm is characterized with better compression performance relative to standard CABAC. In the framework of HEVC encoder 1.6% - 4.5% bitrate reduction was obtained when using improved CABAC instead of the original algorithm.
This paper presents a novel technique of context-based adaptive arithmetic coding of the quantized MDCT coefficients and frequency band gains in audio compression. A key feature of the new technique is combining the c...
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ISBN:
(纸本)9781467325073
This paper presents a novel technique of context-based adaptive arithmetic coding of the quantized MDCT coefficients and frequency band gains in audio compression. A key feature of the new technique is combining the context model in time domain and frequency domain, which used for the quantized norms' and MDCT coefficients' probability. With this new technique, we achieve a high degree of adaptation and redundancy reduction in the adaptive arithmetic coding. In addition, we employ an efficient variable rate algorithm for G.719. The variable rate algorithm is designed based on the baseline entropy coding method of G.719 and the proposed adaptive arithmetic coding technique respectively. For a set of audio samples used in the application, we achieve an average bit-rate saving of 7.2% while producing audio quality equal to that of the original G.719.
In this work, we propose an advanced framework for lossless compression of binary images. The proposed framework is organized in two steps: contour approximation and residue encoding. First, object contours are approx...
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ISBN:
(纸本)9781728129402
In this work, we propose an advanced framework for lossless compression of binary images. The proposed framework is organized in two steps: contour approximation and residue encoding. First, object contours are approximated by piecewise cubic polynomial curves and Improved adaptive arithmetic coding are applied to encode the coefficients. Then, we propose an efficient algorithm based on morphological operation to detect the residues, i.e. the points of the original image that are not in the reconstructed image. After encoding the residue by the proposed column by column method or the Chebyshev distance method, a very high compression ratio can be achieved.
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