Winner-take-all algorithms are commonly used techniques in clustering analysis. However, they have some problems ranging from clusters under utilization to the extended training time. Some solutions to these problems ...
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Winner-take-all algorithms are commonly used techniques in clustering analysis. However, they have some problems ranging from clusters under utilization to the extended training time. Some solutions to these problems are addressed here. It is shown here that using the maximum-likelihood criterion instead of the Euclidean distance metric results in better clustering. The clusters are represented by a set of neuron each has a Gaussian receptive field. For these Gaussian neurons, the covariance matrices, in addition to the centers, are learned. The one-winner condition is relaxed by maximizing the likelihood function of the mixture density function of the samples. This produces larger likelihood values and more normally distributed clusters. A fast mixture likelihood clustering is provided for both batch and pattern learning modes. Convergence analysis and experimental results are also presented.
Recently, a robust and secure image sharing scheme with personal identity information embedded was proposed based on Compressive Sensing, Secret image Sharing and Diffie-Hellman Agreement. However, there exists a secu...
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Recently, a robust and secure image sharing scheme with personal identity information embedded was proposed based on Compressive Sensing, Secret image Sharing and Diffie-Hellman Agreement. However, there exists a security flaw in this scheme. It cannot resist the man-in-the-middle attack in the authentication stage. Anyone can disguise himself as a legal person and get the information when exchanging the secret keys, which provides the possibility for information leakage, tampering, and other attacks. In this paper, we propose an image encryption and compression algorithm with identity authentication and blind signcryption based on Parallel Compressive Sensing (PCS), Secret Sharing(SS) and Elliptic Curve Cryptography (ECC). Firstly, Logistic-Tent system and PCS are employed to complete compression and lightweight encryption in the compression stage. Secondly, random sequences are generated based on Chebyshev map to construct four encryption matrices to perform the encryption process. Meanwhile, the participants' identity authentication and blind signcryption can be achieved by using ECC. Finally, we prove the efficiency and security of the blind signcryption, which can authenticate the participants' identity before restoring the original image. Experiments and security analysis demonstrate that the proposed scheme not only reduce the storage space and computational complexity effectively, but also has resistance against the man-in-the-middle attack, forgery attack and chosen-text attack.
This paper presents methods based on convolutional neural networks (CNNs) for removing compression artifacts. We modify the Inception module for the image restoration problem and use it as a building block for constru...
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This paper presents methods based on convolutional neural networks (CNNs) for removing compression artifacts. We modify the Inception module for the image restoration problem and use it as a building block for constructing blind and non-blind artifact removal networks. It is known that a CNN trained in a non-blind scenario (known compression quality factor) performs better than the one trained in a blind scenario (unknown factor), and our network is not an exception. However, the blind system is more practical because the compression quality factor is not always available or does not reflect the actual quality when the image is a transcoded or requantized image. Hence, in this paper, we also propose a pseudo-blind system that estimates the quality factor for a given compressed image and then applies a network that is trained with a similar quality factor. For this purpose, we propose a CNN that estimates the compression quality factor and prepare several non-blind artifact removal networks that are trained for some specific compression quality factors. We train the networks and conduct experiments on widely used compression standards, such as JPEG, MPEG-2, H.264, and HEVC. In addition, we conduct experiments for dynamically changing and transcoded videos to demonstrate the effectiveness of the quality estimation method. The experimental results show that the proposed pseudo-blind network performs better than the blind one for the various cases stated above and requires fewer computations.
Quality criteria for image coding are often based on mean square error. However, this is not always a relevant measure of visual quality at low bit rates. Here, we investigate the properties of a distortion measure ba...
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Quality criteria for image coding are often based on mean square error. However, this is not always a relevant measure of visual quality at low bit rates. Here, we investigate the properties of a distortion measure based on the conditional differential entropy of the input signal given its quantized value. The proposed measure appears to be a correct representation of the amount of information lost by quantization. An adaptive bit allocation algorithm is proposed in order to take advantage of this criterion. Experimental results illustrate the behavior of the proposed distortion measure and exhibit interesting visual properties for low bit-rate subband image coding.
This paper presents a new decoding method for the improvement of visual quality in block-coned video sequences. A global decoding approach is performed to tackle artifacts die to compression, such as blocking effects ...
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This paper presents a new decoding method for the improvement of visual quality in block-coned video sequences. A global decoding approach is performed to tackle artifacts die to compression, such as blocking effects and quantization noise, and simultaneously losses due to transmission, acquisition, or storage. It is based on the optimization of a criterion allowing independent and adapted restoration for the background of the scene and for each moving object. Experimental results on a MPEG sequence with simulated dropouts, and on an uncompressed hut very corrupted old movie are presented They demonstrate the efficiency of the proposed decoding method.
Side match vector quantization (SMVQ) is an effective coding technique and it has been widely used in low bit rate image compression and data hiding techniques. It utilizes the correlations between neighboring blocks ...
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Side match vector quantization (SMVQ) is an effective coding technique and it has been widely used in low bit rate image compression and data hiding techniques. It utilizes the correlations between neighboring blocks to better generate a small fixed-size state codebook for each input vector. However, compared with vector quantization (VQ) scheme, the quality of SMVQ-coded image is substantially decreased when bit rate (BR) becomes low. This paper proposes a new side match vector quantization scheme which is based on using extend state codebook (ESCSMVQ). Experimental results show that comparing to conventional SMVQ, the bit rate of ESCSMVQ can be significantly reduced meanwhile keeping the exactly same quality of VQ-coded image.
In many state-of-the-art compression systems, signal transformation is an integral part of the encoding and decoding process, where transforms provide compact representations for the signals of interest. This paper in...
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In many state-of-the-art compression systems, signal transformation is an integral part of the encoding and decoding process, where transforms provide compact representations for the signals of interest. This paper introduces a class of transforms called graph-based transforms (GBTs) for video compression, and proposes two different techniques to design GBTs. In the first technique, we formulate an optimization problem to learn graphs from data and provide solutions for optimal separable and nonseparable GBT designs, called GL-GBTs. The optimality of the proposed GL-GBTs is also theoretically analyzed based on Gaussian-Markov random field (GMRF) models for intra and inter predicted block signals. The second technique develops edge-adaptive GBTs (EA-GBTs) in order to flexibly adapt transforms to block signals with image edges (discontinuities). The advantages of EA-GBTs are both theoretically and empirically demonstrated. Our experimental results show that the proposed transforms can significantly outperform the traditional Karhunen-Loeve transform (KLT).
In this article, we propose a privacy-preserving image classification method that uses encrypted images and an isotropic network, such as the vision transformer. The proposed method allows us not only to apply images ...
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In this article, we propose a privacy-preserving image classification method that uses encrypted images and an isotropic network, such as the vision transformer. The proposed method allows us not only to apply images without visual information to deep neural networks for both training and testing, but also to maintain a high classification accuracy. In addition, compressible encrypted images, called encryption-then-compression (EtC) images, can be used for both training and testing without any adaptation network. Previously, to classify EtC images, an adaptation network was required before a classification network, so methods with an adaptation network have been only tested on small images. To the best of our knowledge, previous privacy-preserving image classification methods have never considered image compressibility and patch embedding-based isotropic networks. In an experiment, the proposed privacy-preserving image classification was demonstrated to outperform state-of-the-art methods even when EtC images were used in terms of classification accuracy and robustness against various attacks under the use of two isotropic networks: vision transformer and ConvMixer.
A novel variable-rate image-coding scheme called the predictive subcodebook search algorithm (PSS) is proposed. In PSS, the initial searched codeword for each input vector is predicted by its encoded neighbors and the...
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A novel variable-rate image-coding scheme called the predictive subcodebook search algorithm (PSS) is proposed. In PSS, the initial searched codeword for each input vector is predicted by its encoded neighbors and the closest codeword in the codebook is searched from a specific range (or subcodebook) that is determined dynamically according to the block type of its encoded neighbors. In addition, the relative addressing technique is employed to encode the searched closest code-word. According to the simulation results, it is shown that PSS not only provides better objective image quality peak SNR (PSNR) but also reduces the storage cost for storing multiple codebooks. Furthermore, PSS requires very little computational cost, compared to the full search algorithm for the vector quantization (VQ) scheme. Therefore, it is concluded that PSS indeed provides a good means for variable-rate image coding. (C) 2000 Society of Photo-Optical Instrumentation Engineers.
Two novel real-time software-based video coders, the software-based moving picture coder (SBMPC) and the popular video coder (PVC), are proposed in this paper. SBMPC uses the modified block truncation code and the new...
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Two novel real-time software-based video coders, the software-based moving picture coder (SBMPC) and the popular video coder (PVC), are proposed in this paper. SBMPC uses the modified block truncation code and the newly proposed multiresolution-in-time sampling techniques, PVC uses the adaptive quantizer and the modified windowed Huffman-kind coder, to make both the coding speed and the compression ratio much higher than those of traditional video coders. By using the proposed coders, video quality and coding speed are good and fast enough for practical video applications. Since no compression hardwares are needed,in,the proposed video coders, the cost and complexity of developing multimedia applications can be greatly reduced.
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