In this article, we introduce a new approach to human movement by defining the movement as a stat... more In this article, we introduce a new approach to human movement by defining the movement as a static object or a super object in one two-dimensional image. This method can allow researchers to label and describe the total movement as an object isolated from a reference video. This ap-proach allows us to perform various tasks, including finding similar movements in a video, measuring, and comparing movements, generating new similar movements, and defining chore-ography by controlling specific parameters in the human body skeleton. As a result of the pre-sented approach, we can eliminate the need to label images manually, disregard the problem of finding the beginning and the end of a movement, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one il-lustrates how to verify and score a requested movement. In co...
ITRE 2005. 3rd International Conference on Information Technology: Research and Education, 2005.
Coefficient dropping is a common tool for video transrating in order to adapt it to various netwo... more Coefficient dropping is a common tool for video transrating in order to adapt it to various network bandwidth constraints. Several recent works propose Lagrangian optimization for finding the optimal retained coefficients for each coded block to achieve the desired bit-rate with minimum distortion. In this paper we extend the Lagrangian optimization procedure by modifying the coefficients prior to dropping them.
Machine learning algorithms have become a very essential tool in the fields of math and engineeri... more Machine learning algorithms have become a very essential tool in the fields of math and engineering, as well as for industrial purposes (fabric, medicine, sport, etc.). This research leverages classical machine learning algorithms for innovative accurate and efficient fabric protrusion detection. We present an approach for improving model training with a small dataset. We use a few classic statistics machine learning algorithms (decision trees, logistic regression, etc.) and a fully connected neural network (NN) model. We also present an approach to optimize a model accuracy rate and execution time for finding the best accuracy using parallel processing with Dask (Python).
2018 International Conference on Computing, Networking and Communications (ICNC), 2018
The increasing popularity of online video content and adaptive video streaming services, especial... more The increasing popularity of online video content and adaptive video streaming services, especially ones based on HTTP Adaptive Streaming, highlights the need for streaming optimization solutions. Predicting end users Quality of Experience (QoE) by using machine learning algorithms, may allow content servers to allocate bandwidth smartly and more efficiently. In this work, we present a new user quality of experience prediction algorithm which extracts features based on user traffic pattern parameters such as bit-rate, resolution, frame rate, etc. In order to optimize the features set and the corresponding machine learning algorithms, we have used three different feature selection algorithms and six different classifiers. We show that the Decision Tree algorithm achieved 86% accuracy in predicting the user quality of experience.
International Conference on Information Technology: Research and Education, 2003. Proceedings. ITRE2003., 2003
We suggest a computer simulation environment for analysis of fast motion estimation (ME) algorith... more We suggest a computer simulation environment for analysis of fast motion estimation (ME) algorithms. We present an architecture for algorithm evaluation, ME algorithms, results of computer simulations, and illustrate the analysis with tables, PSNR plots, and performance plots. We also describe multicriteria evaluation of algorithms. Two evaluation strategies are suggested: (i) a strategy with constraints and (ii) a strategy with "near" Pareto-efficient points. Four basic test video sequences were used. Our algorithm evaluation environment was effectively used as a basis for student projects.
Inter-Prediction is used effectively in multiple standards, including H.264 and HEVC (also known ... more Inter-Prediction is used effectively in multiple standards, including H.264 and HEVC (also known as H.265). It leverages correlation between blocks of consecutive video frames in order to perform motion compensation and thus predict block pixel values and reduce transmission bandwidth. In order to reduce the magnitude of the transmitted Motion Vector (MV) and thus reduce bandwidth, the encoder utilizes Predicted Motion Vector (PMV), which is derived by taking the median vector of the corresponding MVs of the neighboring blocks. In this research, we propose innovative methods, based on neural networks prediction, for improving the accuracy of the calculated PMV. We begin by showing a straightforward approach of calculating the best matching PMV and signaling its neighbor block index value to the decoder while reducing the number of bits required to represent the result without adding any computation complexity. Then we use a classification Fully Connected Neural Networks (FCNN) to es...
A novel model for image restoration is discussed where the imagers are digital cameras in space, ... more A novel model for image restoration is discussed where the imagers are digital cameras in space, damaged from cosmic radiation, or ultrasonic medical devices damaged from speckle noise. In previous works we have suggested a simulated annealing algorithm based on Ising theory for the restoration of colored images and videos which were damaged from various kinds of noise. In what follows we will discuss the affinity between statistical physics and image processing and use an adjusted version of our Ising-like algorithm for pixel correction in digital space cameras and noise removal from ultra-sound images. We will present restoration results which are achieved by the combination of known algorithms such as Median and LOCO-I together with Ising-like models. The mean error and PSNR values of the restored images will be shown to exceed the values achieved with similar methods. E. Cohen Department of Exact Sciences, Tel-Aviv University, RamatAviv, Tel-Aviv 69978, Israel Tel.: +972-3-64052...
Applications of Digital Image Processing XLIV, 2021
It is anticipated that in some extreme situations, autonomous cars will benefit from the interven... more It is anticipated that in some extreme situations, autonomous cars will benefit from the intervention of a ״Remote Driver״. The vehicle computer may discover a failure and decide to request remote assistance for safe roadside parking. In a more extreme scenario, the vehicle may require a complete remote-driver takeover due to malfunctions or an inability to resolve unknown decision logic. In such cases, the remote driver will need a sufficiently good quality real-time video stream of the vehicle cameras to respond quickly and accurately enough to the situation at hand. Relaying such a video stream to the remote Command and Control (C&C) center is especially challenging when considering the varying wireless channel bandwidths expected in these scenarios. This paper proposes an innovative end-to-end content-sensitive video compression scheme to allow efficient and satisfactory video transmission from autonomous vehicles to the remote C&C center.
Applications of Digital Image Processing XLII, 2019
Image steganography is the art of hiding information in a cover image in such a way that a third ... more Image steganography is the art of hiding information in a cover image in such a way that a third party does not notice the hidden information. This paper presents a novel technique for image steganography in the spatial domain. The new method hides and recovers hidden information of substantial length within digital imagery, while maintaining the size and quality of the original image. The image gradient is used to generate a saliency image, which represent the energy of each pixel in the image. Pixels with higher energy are more salient and they are valuable for hiding data since their visual impairment is low. From the saliency image, a cumulative maximum energy matrix is created; this matrix is used to generate horizontal seams that pass over the maximum energy path. By embedding the secret bits of information along the seams, a stego-image is created which contains the hidden message. In the stegoimage, we ensure that the hidden data is invisible, with very small perceived image quality degradation. The same algorithms are used to reconstruct the hidden message from the stego-image. Experiments have been conducted using two types of image and two types of hidden data to evaluate the proposed technique. The experimental results show that the proposed algorithm has a high capacity and good invisibility, with a Peak Signal-to-Noise Ratio (PSNR) of about 70, and a Structural SIMilarity index (SSIM) of about 1.
Multidisciplinary Approach to Modern Digital Steganography, 2021
Steganographic channels can be abused for malicious purposes, thus raising the need to detect mal... more Steganographic channels can be abused for malicious purposes, thus raising the need to detect malicious embedded steganographic information (steganalysis). This chapter will cover the little-studied problem of steganography and steganalysis over a noisy channel, providing a detailed modeling for the special case of spatial-domain image steganography. It will approach these issues from both a theoretical and a practical point of view. After a description of spatial-domain image steganography, the impact of Gaussian noise and packet loss on the steganographic channel will be discussed. Characterization of the substitution-insertion-deletion (SID) channel parameters will be performed through experiments on a large number of images from the ALASKA database. Finally, a steganalysis technique for error-affected spatial-domain image steganography using a convolutional neural network (CNN) will be introduced, studying the relationship between different types and levels of distortions and th...
Informatsionno-upravliaiushchie sistemy (Information and Control Systems), 2017
The present monograph conceptually describes the main elements and fundamental aspects of reconst... more The present monograph conceptually describes the main elements and fundamental aspects of reconstructing information data in multimedia (radio and optical) wired and wireless communication links and systems, corrupted by inner and outer fading phenomena. The monograph describes the basic aspects and the corresponding mathematical models of all types of fading – slow and fast, with corresponding probability distribution functions – Gaussian, Rayleigh and Ricean, modified for the corresponding types of noise – flat and frequency/time-selective, occurring in fading radio and optical communication links. In addition, the monograph considers existing models and new ones, proposed by the authors,for avoidance offading corruption effects on data stream parameters such as the maximum data rate, link capacity, linkspectral efficiency, and bit-error-rate (BER), which cause loss of bits, pixels and frames in decoded visual and non-visual data messages. Moreover, the reader will gainknowledge on how to reconstruct lossesblock of pixels and frames, corrupted by various typesof fading,which occurin wired and/or wireless channels, radio and/or optic linksofmodern communication systems.
In this article, we introduce a new approach to human movement by defining the movement as a stat... more In this article, we introduce a new approach to human movement by defining the movement as a static object or a super object in one two-dimensional image. This method can allow researchers to label and describe the total movement as an object isolated from a reference video. This ap-proach allows us to perform various tasks, including finding similar movements in a video, measuring, and comparing movements, generating new similar movements, and defining chore-ography by controlling specific parameters in the human body skeleton. As a result of the pre-sented approach, we can eliminate the need to label images manually, disregard the problem of finding the beginning and the end of a movement, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one il-lustrates how to verify and score a requested movement. In co...
ITRE 2005. 3rd International Conference on Information Technology: Research and Education, 2005.
Coefficient dropping is a common tool for video transrating in order to adapt it to various netwo... more Coefficient dropping is a common tool for video transrating in order to adapt it to various network bandwidth constraints. Several recent works propose Lagrangian optimization for finding the optimal retained coefficients for each coded block to achieve the desired bit-rate with minimum distortion. In this paper we extend the Lagrangian optimization procedure by modifying the coefficients prior to dropping them.
Machine learning algorithms have become a very essential tool in the fields of math and engineeri... more Machine learning algorithms have become a very essential tool in the fields of math and engineering, as well as for industrial purposes (fabric, medicine, sport, etc.). This research leverages classical machine learning algorithms for innovative accurate and efficient fabric protrusion detection. We present an approach for improving model training with a small dataset. We use a few classic statistics machine learning algorithms (decision trees, logistic regression, etc.) and a fully connected neural network (NN) model. We also present an approach to optimize a model accuracy rate and execution time for finding the best accuracy using parallel processing with Dask (Python).
2018 International Conference on Computing, Networking and Communications (ICNC), 2018
The increasing popularity of online video content and adaptive video streaming services, especial... more The increasing popularity of online video content and adaptive video streaming services, especially ones based on HTTP Adaptive Streaming, highlights the need for streaming optimization solutions. Predicting end users Quality of Experience (QoE) by using machine learning algorithms, may allow content servers to allocate bandwidth smartly and more efficiently. In this work, we present a new user quality of experience prediction algorithm which extracts features based on user traffic pattern parameters such as bit-rate, resolution, frame rate, etc. In order to optimize the features set and the corresponding machine learning algorithms, we have used three different feature selection algorithms and six different classifiers. We show that the Decision Tree algorithm achieved 86% accuracy in predicting the user quality of experience.
International Conference on Information Technology: Research and Education, 2003. Proceedings. ITRE2003., 2003
We suggest a computer simulation environment for analysis of fast motion estimation (ME) algorith... more We suggest a computer simulation environment for analysis of fast motion estimation (ME) algorithms. We present an architecture for algorithm evaluation, ME algorithms, results of computer simulations, and illustrate the analysis with tables, PSNR plots, and performance plots. We also describe multicriteria evaluation of algorithms. Two evaluation strategies are suggested: (i) a strategy with constraints and (ii) a strategy with "near" Pareto-efficient points. Four basic test video sequences were used. Our algorithm evaluation environment was effectively used as a basis for student projects.
Inter-Prediction is used effectively in multiple standards, including H.264 and HEVC (also known ... more Inter-Prediction is used effectively in multiple standards, including H.264 and HEVC (also known as H.265). It leverages correlation between blocks of consecutive video frames in order to perform motion compensation and thus predict block pixel values and reduce transmission bandwidth. In order to reduce the magnitude of the transmitted Motion Vector (MV) and thus reduce bandwidth, the encoder utilizes Predicted Motion Vector (PMV), which is derived by taking the median vector of the corresponding MVs of the neighboring blocks. In this research, we propose innovative methods, based on neural networks prediction, for improving the accuracy of the calculated PMV. We begin by showing a straightforward approach of calculating the best matching PMV and signaling its neighbor block index value to the decoder while reducing the number of bits required to represent the result without adding any computation complexity. Then we use a classification Fully Connected Neural Networks (FCNN) to es...
A novel model for image restoration is discussed where the imagers are digital cameras in space, ... more A novel model for image restoration is discussed where the imagers are digital cameras in space, damaged from cosmic radiation, or ultrasonic medical devices damaged from speckle noise. In previous works we have suggested a simulated annealing algorithm based on Ising theory for the restoration of colored images and videos which were damaged from various kinds of noise. In what follows we will discuss the affinity between statistical physics and image processing and use an adjusted version of our Ising-like algorithm for pixel correction in digital space cameras and noise removal from ultra-sound images. We will present restoration results which are achieved by the combination of known algorithms such as Median and LOCO-I together with Ising-like models. The mean error and PSNR values of the restored images will be shown to exceed the values achieved with similar methods. E. Cohen Department of Exact Sciences, Tel-Aviv University, RamatAviv, Tel-Aviv 69978, Israel Tel.: +972-3-64052...
Applications of Digital Image Processing XLIV, 2021
It is anticipated that in some extreme situations, autonomous cars will benefit from the interven... more It is anticipated that in some extreme situations, autonomous cars will benefit from the intervention of a ״Remote Driver״. The vehicle computer may discover a failure and decide to request remote assistance for safe roadside parking. In a more extreme scenario, the vehicle may require a complete remote-driver takeover due to malfunctions or an inability to resolve unknown decision logic. In such cases, the remote driver will need a sufficiently good quality real-time video stream of the vehicle cameras to respond quickly and accurately enough to the situation at hand. Relaying such a video stream to the remote Command and Control (C&C) center is especially challenging when considering the varying wireless channel bandwidths expected in these scenarios. This paper proposes an innovative end-to-end content-sensitive video compression scheme to allow efficient and satisfactory video transmission from autonomous vehicles to the remote C&C center.
Applications of Digital Image Processing XLII, 2019
Image steganography is the art of hiding information in a cover image in such a way that a third ... more Image steganography is the art of hiding information in a cover image in such a way that a third party does not notice the hidden information. This paper presents a novel technique for image steganography in the spatial domain. The new method hides and recovers hidden information of substantial length within digital imagery, while maintaining the size and quality of the original image. The image gradient is used to generate a saliency image, which represent the energy of each pixel in the image. Pixels with higher energy are more salient and they are valuable for hiding data since their visual impairment is low. From the saliency image, a cumulative maximum energy matrix is created; this matrix is used to generate horizontal seams that pass over the maximum energy path. By embedding the secret bits of information along the seams, a stego-image is created which contains the hidden message. In the stegoimage, we ensure that the hidden data is invisible, with very small perceived image quality degradation. The same algorithms are used to reconstruct the hidden message from the stego-image. Experiments have been conducted using two types of image and two types of hidden data to evaluate the proposed technique. The experimental results show that the proposed algorithm has a high capacity and good invisibility, with a Peak Signal-to-Noise Ratio (PSNR) of about 70, and a Structural SIMilarity index (SSIM) of about 1.
Multidisciplinary Approach to Modern Digital Steganography, 2021
Steganographic channels can be abused for malicious purposes, thus raising the need to detect mal... more Steganographic channels can be abused for malicious purposes, thus raising the need to detect malicious embedded steganographic information (steganalysis). This chapter will cover the little-studied problem of steganography and steganalysis over a noisy channel, providing a detailed modeling for the special case of spatial-domain image steganography. It will approach these issues from both a theoretical and a practical point of view. After a description of spatial-domain image steganography, the impact of Gaussian noise and packet loss on the steganographic channel will be discussed. Characterization of the substitution-insertion-deletion (SID) channel parameters will be performed through experiments on a large number of images from the ALASKA database. Finally, a steganalysis technique for error-affected spatial-domain image steganography using a convolutional neural network (CNN) will be introduced, studying the relationship between different types and levels of distortions and th...
Informatsionno-upravliaiushchie sistemy (Information and Control Systems), 2017
The present monograph conceptually describes the main elements and fundamental aspects of reconst... more The present monograph conceptually describes the main elements and fundamental aspects of reconstructing information data in multimedia (radio and optical) wired and wireless communication links and systems, corrupted by inner and outer fading phenomena. The monograph describes the basic aspects and the corresponding mathematical models of all types of fading – slow and fast, with corresponding probability distribution functions – Gaussian, Rayleigh and Ricean, modified for the corresponding types of noise – flat and frequency/time-selective, occurring in fading radio and optical communication links. In addition, the monograph considers existing models and new ones, proposed by the authors,for avoidance offading corruption effects on data stream parameters such as the maximum data rate, link capacity, linkspectral efficiency, and bit-error-rate (BER), which cause loss of bits, pixels and frames in decoded visual and non-visual data messages. Moreover, the reader will gainknowledge on how to reconstruct lossesblock of pixels and frames, corrupted by various typesof fading,which occurin wired and/or wireless channels, radio and/or optic linksofmodern communication systems.
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