We present the method of complementary ensemble empirical mode decomposition (CEEMD) and Hilbert-Huang transform (HHT) for analyzing nonstationary financial time series. This noise-assisted approach decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the associated instantaneous energy-frequency spectrum to illustrate the properties of various timescales embedded in the original time series. Using HHT, we generate a collection of new features and integrate them into machine learning models, such as regression tree ensemble, support vector machine (SVM), and long short-term memory (LSTM) neural network. Using empirical financial data, we compare several HHT-enhanced machine learning models in terms of forecasting performance.
{"title":"Financial Time Series Analysis and Forecasting with HHT Feature Generation and Machine Learning","authors":"Tim Leung, Theodore Zhao","doi":"10.2139/ssrn.3595914","DOIUrl":"https://doi.org/10.2139/ssrn.3595914","url":null,"abstract":"We present the method of complementary ensemble empirical mode decomposition (CEEMD) and Hilbert-Huang transform (HHT) for analyzing nonstationary financial time series. This noise-assisted approach decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the associated instantaneous energy-frequency spectrum to illustrate the properties of various timescales embedded in the original time series. Using HHT, we generate a collection of new features and integrate them into machine learning models, such as regression tree ensemble, support vector machine (SVM), and long short-term memory (LSTM) neural network. Using empirical financial data, we compare several HHT-enhanced machine learning models in terms of forecasting performance.","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"1025 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116247478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-30DOI: 10.15587/1729-4061.2020.200282
O. Y. Abdulhammed
In image steganography, secret communication is implemented to hide secret information into the cover image (used as the carrier to embed secret information) and generate a stego-image (generated image carrying hidden secret information). Nature provides many ideas for computer scientists. One of these ideas is the orderly way in which the organisms work in nature when they are in groups. If the group itself is treated as an individual (the swarm), the swarm is more intelligent than any individual in the group. Crow Search Algorithm (CSA) is a meta-heuristic optimizer where individuals emulate the intelligent behavior in a group of crows. It is based on simulating the intelligent behavior of crow flocks and attempts to imitate the social intelligence of a crow flock in their food gathering process. This paper presents a novel meta-heuristic approach based on the Crow Search Algorithm (CSA), where at the beginning the color cover image is converted into three channels (RGB) and then those channels are converted into three spaces, which are Y, Cb, Cr. After applying Discrete wavelet transform (DWT) on each space separately, the CSA algorithm is used on each space (YCbCr) to find the best location that will be used to hide secret information, the CSA is used to increase the security force by finding the best locations that have high frequency and are invulnerable to attacks, the DWT is used to increase robustness against noise. The proposed system is implemented on three fingerprint cover images for experiments, for the quality of stego image the histogram, Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Number of Pixel Change Rate Test (NPCR), Structural Similarity Index Metric (SSIM) and Correlation Coefficients (CC) are computed. The result demonstrated the strength of the CSA to hide data, also discovered that using CSA may lead to finding favorable results compared to the other algorithms
{"title":"Strengthening Steganoghraphy by Using Crow Search Algorithm of Fingerprint Image","authors":"O. Y. Abdulhammed","doi":"10.15587/1729-4061.2020.200282","DOIUrl":"https://doi.org/10.15587/1729-4061.2020.200282","url":null,"abstract":"In image steganography, secret communication is implemented to hide secret information into the cover image (used as the carrier to embed secret information) and generate a stego-image (generated image carrying hidden secret information). Nature provides many ideas for computer scientists. One of these ideas is the orderly way in which the organisms work in nature when they are in groups. If the group itself is treated as an individual (the swarm), the swarm is more intelligent than any individual in the group. Crow Search Algorithm (CSA) is a meta-heuristic optimizer where individuals emulate the intelligent behavior in a group of crows. It is based on simulating the intelligent behavior of crow flocks and attempts to imitate the social intelligence of a crow flock in their food gathering process. This paper presents a novel meta-heuristic approach based on the Crow Search Algorithm (CSA), where at the beginning the color cover image is converted into three channels (RGB) and then those channels are converted into three spaces, which are Y, Cb, Cr. After applying Discrete wavelet transform (DWT) on each space separately, the CSA algorithm is used on each space (YCbCr) to find the best location that will be used to hide secret information, the CSA is used to increase the security force by finding the best locations that have high frequency and are invulnerable to attacks, the DWT is used to increase robustness against noise. The proposed system is implemented on three fingerprint cover images for experiments, for the quality of stego image the histogram, Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Number of Pixel Change Rate Test (NPCR), Structural Similarity Index Metric (SSIM) and Correlation Coefficients (CC) are computed. The result demonstrated the strength of the CSA to hide data, also discovered that using CSA may lead to finding favorable results compared to the other algorithms","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133114705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-30DOI: 10.15587/1729-4061.2020.201731
N. Kalashnikov, Olexandr Kokhanov, O. Iakovenko, N. Kushnirenko
This paper addresses the task of developing a steganographic method to hide information, resistant to analysis based on the Rich model (which includes several different submodels), using statistical indicators for the distribution of the pairs of coefficients for a discrete cosine transform (DCT) with different values. This type of analysis implies calculating the number of DCT coefficients pairs, whose coordinates in the frequency domain differ by a fixed quantity (the offset). Based on these values, a classifier is trained for a certain large enough data sample, which, based on the distribution of the DCT coefficients pairs for an individual image, determines the presence of additional information in it. A method based on the preliminary container modification before embedding a message has been proposed to mitigate the probability of hidden message detection. The so-called Generative Adversarial Network (GAN), consisting of two related neural networks, generator and discriminator, was used for the modification. The generator creates a modified image based on the original container; the discriminator verifies the degree to which the modified image is close to the preset one and provides feedback for the generator. By using a GAN, based on the original container, such a modified container is generated so that, following the embedding of a known steganographic message, the distribution of DCT coefficients pairs is maximally close to the indicators of the original container. We have simulated the operation of the proposed modification; based on the simulation results, the probabilities have been computed of the proper detection of the hidden information in the container when it was modified and when it was not. The simulation results have shown that the application of the modification based on modern information technologies (such as machine learning and neural networks) could significantly reduce the likelihood of message detection and improve the resistance against a steganographic analysis
{"title":"A Steganographic Method of Improved Resistance to the Rich Model based Analysis","authors":"N. Kalashnikov, Olexandr Kokhanov, O. Iakovenko, N. Kushnirenko","doi":"10.15587/1729-4061.2020.201731","DOIUrl":"https://doi.org/10.15587/1729-4061.2020.201731","url":null,"abstract":"This paper addresses the task of developing a steganographic method to hide information, resistant to analysis based on the Rich model (which includes several different submodels), using statistical indicators for the distribution of the pairs of coefficients for a discrete cosine transform (DCT) with different values. This type of analysis implies calculating the number of DCT coefficients pairs, whose coordinates in the frequency domain differ by a fixed quantity (the offset). Based on these values, a classifier is trained for a certain large enough data sample, which, based on the distribution of the DCT coefficients pairs for an individual image, determines the presence of additional information in it. A method based on the preliminary container modification before embedding a message has been proposed to mitigate the probability of hidden message detection. The so-called Generative Adversarial Network (GAN), consisting of two related neural networks, generator and discriminator, was used for the modification. The generator creates a modified image based on the original container; the discriminator verifies the degree to which the modified image is close to the preset one and provides feedback for the generator. By using a GAN, based on the original container, such a modified container is generated so that, following the embedding of a known steganographic message, the distribution of DCT coefficients pairs is maximally close to the indicators of the original container. We have simulated the operation of the proposed modification; based on the simulation results, the probabilities have been computed of the proper detection of the hidden information in the container when it was modified and when it was not. The simulation results have shown that the application of the modification based on modern information technologies (such as machine learning and neural networks) could significantly reduce the likelihood of message detection and improve the resistance against a steganographic analysis","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132961431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents an overview of image steganography techniques. In the present paper, the main emphasis is given on transform domain techniques including a brief introduction on spatial domain techniques as well. The major advantage with transform domain technique is that the data is concealed in every bit of cover image and almost impossible for an intruder to get unauthorized access to it. The study analyzes different transforms based techniques including many variants of Wavelet Transform with its advantages, challenges and applications.
{"title":"A Brief Review on Image Steganography Techniques","authors":"R. Ruchi, U. Ghanekar","doi":"10.2139/ssrn.3579269","DOIUrl":"https://doi.org/10.2139/ssrn.3579269","url":null,"abstract":"This paper presents an overview of image steganography techniques. In the present paper, the main emphasis is given on transform domain techniques including a brief introduction on spatial domain techniques as well. The major advantage with transform domain technique is that the data is concealed in every bit of cover image and almost impossible for an intruder to get unauthorized access to it. The study analyzes different transforms based techniques including many variants of Wavelet Transform with its advantages, challenges and applications.","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"209 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132701615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gesture Recognition and by definition Image Detection is a key research area because of its varied application in fields such as Sign Language Detection, Gesture Recognition where each gesture whether it is done by hand, objects or otherwise can be detected and understood by a computer. Increasing the accuracy, functionality and speed of the process can help to recognition and detection fast and easy where it can be used in real-time to not only understand gestures but also recognition of images pertaining to a certain topic like use in medical imagery and so on.
{"title":"Comparative Analysis of Methods of Gesture Recognition in Image Processing","authors":"Preyas Hanche, Akash Dubey, Ayush Falor","doi":"10.2139/ssrn.3608762","DOIUrl":"https://doi.org/10.2139/ssrn.3608762","url":null,"abstract":"Gesture Recognition and by definition Image Detection is a key research area because of its varied application in fields such as Sign Language Detection, Gesture Recognition where each gesture whether it is done by hand, objects or otherwise can be detected and understood by a computer. Increasing the accuracy, functionality and speed of the process can help to recognition and detection fast and easy where it can be used in real-time to not only understand gestures but also recognition of images pertaining to a certain topic like use in medical imagery and so on.","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123510718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Listening to music is a very common thing. Nowadays, users pick music manually on their own i.e. the music has to be chosen manually. So, to ease the work of the users, expression recognition plays an important role in predicting and deciding the mood. It uses different facial features to detect mood. After a certain mood is detected, the system will play music according to the mood. In this system, machine learning techniques and algorithms such as SVM, Neural Networks, Image Preprocessing are used. Till now, the research has shown accuracy up to 72.4% by using SVM.
{"title":"Emotion Recognition with Music using Facial Feature Extraction and Deep Learning","authors":"A. Dhar, Bilal N Shaikh Mohammad","doi":"10.2139/ssrn.3560840","DOIUrl":"https://doi.org/10.2139/ssrn.3560840","url":null,"abstract":"Listening to music is a very common thing. Nowadays, users pick music manually on their own i.e. the music has to be chosen manually. So, to ease the work of the users, expression recognition plays an important role in predicting and deciding the mood. It uses different facial features to detect mood. After a certain mood is detected, the system will play music according to the mood. In this system, machine learning techniques and algorithms such as SVM, Neural Networks, Image Preprocessing are used. Till now, the research has shown accuracy up to 72.4% by using SVM.","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126124314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since the ancient times herbal plants are being used for health wellness. But due to modern life style and dependences on allopathic medicine, the majority of the population is unaware about the usages and faced difficulty to identify the herbal plant. These plants are widely used in the area of research where recognition of useful/beneficial plants located in nearby locality becomes necessity of people, so they can take advantage in their daily life to cure diseases. It is apparent that there in need of machine which can automatically recognize the herbal plant. Such machines need to be training for plant recognition. Several physical features like roots, stem, leaf pattern, color, shape of leaf, number of petals are used to recognize and identify plants. Most prominent organ is the leaf shape as it is available throughout the year. Therefore, researchers considered leaf as an important part to recognized plant easily and accurately. In this paper main focus on plant recognition through leaf features using machine learning concepts so to achieve the desired goal on time and in specific conditions.
{"title":"Review of Machine Learning Herbal Plant Recognition System","authors":"P. Kaur, Sukhdev Singh, Monika Pathak","doi":"10.2139/ssrn.3565850","DOIUrl":"https://doi.org/10.2139/ssrn.3565850","url":null,"abstract":"Since the ancient times herbal plants are being used for health wellness. But due to modern life style and dependences on allopathic medicine, the majority of the population is unaware about the usages and faced difficulty to identify the herbal plant. These plants are widely used in the area of research where recognition of useful/beneficial plants located in nearby locality becomes necessity of people, so they can take advantage in their daily life to cure diseases. It is apparent that there in need of machine which can automatically recognize the herbal plant. Such machines need to be training for plant recognition. Several physical features like roots, stem, leaf pattern, color, shape of leaf, number of petals are used to recognize and identify plants. Most prominent organ is the leaf shape as it is available throughout the year. Therefore, researchers considered leaf as an important part to recognized plant easily and accurately. In this paper main focus on plant recognition through leaf features using machine learning concepts so to achieve the desired goal on time and in specific conditions.","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133332470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-29DOI: 10.15587/1729-4061.2020.197095
J. Álvarez-Cedillo, Mario Aguilar-Fernández, T. Álvarez-Sánchez, R. Sandoval-Gómez
In computer vision and image processing, image segmentation remains a relevant research area that contains many partially answered research questions. One of the fields of most significant interest in Digital Image Processing corresponds to segmentation, a process that breaks down an image into its different components that make it up. A technique widely used in the literature is called Region Growing, this technique makes the identification of textures, through the use of characteristic and particular vectors. However, the level of its computational complexity is high. The traditional methods of Region growing are based on the comparison of grey levels of neighbouring pixels, and usually, fail when the region to be segmented contains intensities similar to adjacent regions. However, if a broad tolerance is indicated in its thresholds, the detected limits will exceed the region to identify; on the contrary, if the threshold tolerance decreases too much, the identified region will be less than the desired one. In the analysis of textures, multiple scenes can be seen as the composition of different textures. The visual texture refers to the impression of roughness or smoothness that some surfaces created by the variations of tones or repetition of visual patterns therein. The texture analysis techniques are based on the assignment of one or several parameters indicating the characteristics of the texture present to each region of the image. This paper shows how a parallel algorithm was implemented to solve open problems in the area of image segmentation research. Region growing is an advanced approach to image segmentation in which neighbouring pixels are examined one by one and added to an appropriate region class if no border is detected. This process is iterative for each pixel within the boundary of the region. If adjacent regions are found, a region fusion algorithm is used in which weak edges dissolve, and firm edges remain intact, this requires a lot of processing time on a computer to make parallel implementation possible
{"title":"Implementation of a Parallel Algorithm of Image Segmentation Based on Region Growing","authors":"J. Álvarez-Cedillo, Mario Aguilar-Fernández, T. Álvarez-Sánchez, R. Sandoval-Gómez","doi":"10.15587/1729-4061.2020.197095","DOIUrl":"https://doi.org/10.15587/1729-4061.2020.197095","url":null,"abstract":"In computer vision and image processing, image segmentation remains a relevant research area that contains many partially answered research questions. One of the fields of most significant interest in Digital Image Processing corresponds to segmentation, a process that breaks down an image into its different components that make it up. A technique widely used in the literature is called Region Growing, this technique makes the identification of textures, through the use of characteristic and particular vectors. However, the level of its computational complexity is high. The traditional methods of Region growing are based on the comparison of grey levels of neighbouring pixels, and usually, fail when the region to be segmented contains intensities similar to adjacent regions. However, if a broad tolerance is indicated in its thresholds, the detected limits will exceed the region to identify; on the contrary, if the threshold tolerance decreases too much, the identified region will be less than the desired one. In the analysis of textures, multiple scenes can be seen as the composition of different textures. The visual texture refers to the impression of roughness or smoothness that some surfaces created by the variations of tones or repetition of visual patterns therein. The texture analysis techniques are based on the assignment of one or several parameters indicating the characteristics of the texture present to each region of the image. This paper shows how a parallel algorithm was implemented to solve open problems in the area of image segmentation research. Region growing is an advanced approach to image segmentation in which neighbouring pixels are examined one by one and added to an appropriate region class if no border is detected. This process is iterative for each pixel within the boundary of the region. If adjacent regions are found, a region fusion algorithm is used in which weak edges dissolve, and firm edges remain intact, this requires a lot of processing time on a computer to make parallel implementation possible","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130621938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Look technique utilized for the content gives semantically significant outcome, however isn't a similar with regards to the scan strategy utilized for pictures. Interactive media information is being distributed on the Web at an extraordinary rate. Likewise, in this time of innovation, it is conceivable to get data about any person from web. It has turned out to be fundamental to perform picture hunt of a person to recover the comparative pictures from Web. It is even conceivable to get any kind of data about any superstar from Wikipedia and different locales. This project aims at building the Image Search Engine for recovering the pictures just as structure the profile of a person, from World Wide Web. This is finished via preparing set of pictures of an individual and after that the web crawler creeps over the connections for getting the pertinent pictures. These recovered pictures coordinate with the name entered by the client. A similar outcome is utilized to get the data and manufacture the profile of a similar individual by slithering over the connections.
{"title":"Image Search Engine and Individual Profile Building","authors":"Shravya G, S. G. R.","doi":"10.2139/ssrn.3527541","DOIUrl":"https://doi.org/10.2139/ssrn.3527541","url":null,"abstract":"Look technique utilized for the content gives semantically significant outcome, however isn't a similar with regards to the scan strategy utilized for pictures. Interactive media information is being distributed on the Web at an extraordinary rate. Likewise, in this time of innovation, it is conceivable to get data about any person from web. It has turned out to be fundamental to perform picture hunt of a person to recover the comparative pictures from Web. It is even conceivable to get any kind of data about any superstar from Wikipedia and different locales. This project aims at building the Image Search Engine for recovering the pictures just as structure the profile of a person, from World Wide Web. This is finished via preparing set of pictures of an individual and after that the web crawler creeps over the connections for getting the pertinent pictures. These recovered pictures coordinate with the name entered by the client. A similar outcome is utilized to get the data and manufacture the profile of a similar individual by slithering over the connections.","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125650502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.5121/VLSIC.2019.10201
Jaya Koshta, K. Khare, M. K. Gupta
Video Compression is very essential to meet the technological demands such as low power, less memory and fast transfer rate for different range of devices and for various multimedia applications. Video compression is primarily achieved by Motion Estimation (ME) process in any video encoder which contributes to significant compression gain.Sum of Absolute Difference (SAD) is used as distortion metric in ME process.In this paper, efficient Absolute Difference (AD) circuit is proposed which uses Brent Kung Adder(BKA) and a comparator based on modified 1’s complement principle and conditional sum adder scheme. Results shows that proposed architecture reduces delay by 15% and number of slice LUTs by 42% as compared to conventional architecture. Simulation and synthesis are done on Xilinx ISE 14.2 using Virtex 7 FPGA.
视频压缩技术是满足低功耗、低内存、快传输速率等技术要求的重要技术手段。在任何视频编码器中，视频压缩主要是通过运动估计(ME)过程来实现的，这有助于显著的压缩增益。在ME过程中，采用绝对差和(SAD)作为失真度量。本文提出了一种采用Brent Kung加法器(BKA)和基于修正1补码原理和条件和加法器的比较器的高效绝对差分(AD)电路。结果表明，与传统架构相比，该架构可减少15%的延迟和42%的片lut数量。仿真和综合在Xilinx ISE 14.2上使用Virtex 7 FPGA完成。
{"title":"Efficient Absolute Difference Circuit for SAD Computation On FPGA","authors":"Jaya Koshta, K. Khare, M. K. Gupta","doi":"10.5121/VLSIC.2019.10201","DOIUrl":"https://doi.org/10.5121/VLSIC.2019.10201","url":null,"abstract":"Video Compression is very essential to meet the technological demands such as low power, less memory and fast transfer rate for different range of devices and for various multimedia applications. Video compression is primarily achieved by Motion Estimation (ME) process in any video encoder which contributes to significant compression gain.Sum of Absolute Difference (SAD) is used as distortion metric in ME process.In this paper, efficient Absolute Difference (AD) circuit is proposed which uses Brent Kung Adder(BKA) and a comparator based on modified 1’s complement principle and conditional sum adder scheme. Results shows that proposed architecture reduces delay by 15% and number of slice LUTs by 42% as compared to conventional architecture. Simulation and synthesis are done on Xilinx ISE 14.2 using Virtex 7 FPGA.","PeriodicalId":433297,"journal":{"name":"EngRN: Signal Processing (Topic)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117302139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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