Pub Date : 2020-07-28DOI: 10.4236/jsip.2020.112002
B. Kerim
Driving in fog condition is dangerous. Fog causes poor visibility on roads leading to road traffic accident (RTA). RTA in Albaha is common because of its rough terrain, in addition to the climate that is mainly rainy and foggy. The rain season in Albaha region begins in October to February characterized by rainfall and fog. Many studies have reported the adverse effects of the rain on RTA which results in an increased rate of crashes. On the other hand, Albaha region is not supported by a proper intelligent transportation system and infrastructure. Thus, a Driver Assistance System (DAS) that requires minimum infrastructure is needed. A DAS under fog called No_Collision has been developed by a researcher in Albaha University. This paper discusses an implementation of adaptive Kalman Filter by utilizing Fuzzy logic system with the aim to improve the accuracy of position and velocity prediction of the No_Collision system. The experiment results show a promising adaptive system that reduces the error percentage of the prediction up to 56.58%.
{"title":"Improving the Accuracy of Under-Fog Driving Assistance System","authors":"B. Kerim","doi":"10.4236/jsip.2020.112002","DOIUrl":"https://doi.org/10.4236/jsip.2020.112002","url":null,"abstract":"Driving in fog condition is dangerous. Fog \u0000causes poor visibility on roads leading to road traffic accident (RTA). RTA in \u0000Albaha is common because of its rough terrain, in addition to the climate that \u0000is mainly rainy and foggy. The rain season in Albaha region begins in October \u0000to February characterized by rainfall and fog. Many studies have reported the \u0000adverse effects of the rain on RTA which results in an increased rate of \u0000crashes. On the other hand, Albaha region is not supported by a proper \u0000intelligent transportation system and infrastructure. Thus, a Driver Assistance \u0000System (DAS) that requires minimum infrastructure is needed. A DAS under fog \u0000called No_Collision has been developed by a researcher in Albaha University. \u0000This paper discusses an implementation of adaptive Kalman Filter by utilizing \u0000Fuzzy logic system with the aim to improve the accuracy of position and \u0000velocity prediction of the No_Collision system. The experiment results show a \u0000promising adaptive system that reduces the error percentage of the prediction \u0000up to 56.58%.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82620064","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 : 2020-01-01DOI: 10.4236/jsip.2020.114006
Eryi Hu
In order to study the strength of the composite material plate problems, need to adopt a nondestructive testing method to obtain the specimen surface under the effect of high-speed impact regularity of shape. The projection profilometry was used to measure the surface profile or the full field deformation. Furtherly, by using the Fourier transform algorithm, there is only one frame of captured image which is needed in the measurement, so that it can be introduced into the high speed impaction procedure measurement. An experimental system, which was contained with an impact setup and the projection profilometry measurement part, was constructed for the impaction action characteristic research. The metallic impact object can be launched by a gas gun or a spin fan, respectively. The detected object is manufactured by composite materials. In order to increase the surface deformation measurement accuracy, the calibration method and the error was discussed with different calibration specimen. And then, the proposed profilometry measurement method is proved by the gas gun and spin fan projectile test. The surface deformation of the manufactured composite plates and fan case are measured in the impaction procedure. So that the impact action details can be described much more clearly than the traditional video monitoring method.
{"title":"Application of 3D Projection Profilometry in the High Speed Impaction Surface Deformation Measurement Research","authors":"Eryi Hu","doi":"10.4236/jsip.2020.114006","DOIUrl":"https://doi.org/10.4236/jsip.2020.114006","url":null,"abstract":"In order to study the strength of the composite material plate problems, need to adopt a nondestructive testing method to obtain the specimen surface under the effect of high-speed impact regularity of shape. The projection profilometry was used to measure the surface profile or the full field deformation. Furtherly, by using the Fourier transform algorithm, there is only one frame of captured image which is needed in the measurement, so that it can be introduced into the high speed impaction procedure measurement. An experimental system, which was contained with an impact setup and the projection profilometry measurement part, was constructed for the impaction action characteristic research. The metallic impact object can be launched by a gas gun or a spin fan, respectively. The detected object is manufactured by composite materials. In order to increase the surface deformation measurement accuracy, the calibration method and the error was discussed with different calibration specimen. And then, the proposed profilometry measurement method is proved by the gas gun and spin fan projectile test. The surface deformation of the manufactured composite plates and fan case are measured in the impaction procedure. So that the impact action details can be described much more clearly than the traditional video monitoring method.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"27 1","pages":"103-115"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80808637","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}
Automatic speech recognition (ASR) is vital for very low-resource languages for mitigating the extinction trouble. Chaha is one of the low-resource languages, which suffers from the problem of resource insufficiency and some of its phonological, morphological, and orthographic features challenge the development and initiatives in the area of ASR. By considering these challenges, this study is the first endeavor, which analyzed the characteristics of the language, prepared speech corpus, and developed different ASR systems. A small 3-hour read speech corpus was prepared and transcribed. Different basic and rounded phone unit-based speech recognizers were explored using multilingual deep neural network (DNN) modeling methods. The experimental results demonstrated that all the basic phone and rounded phone unit-based multilingual models outperformed the corresponding unilingual models with the relative performance improvements of 5.47% to 19.87% and 5.74% to 16.77%, respectively. The rounded phone unit-based multilingual models outperformed the equivalent basic phone unit-based models with relative performance improvements of 0.95% to 4.98%. Overall, we discovered that multilingual DNN modeling methods are profoundly effective to develop Chaha speech recognizers. Both the basic and rounded phone acoustic units are convenient to build Chaha ASR system. However, the rounded phone unit-based models are superior in performance and faster in recognition speed over the corresponding basic phone unit-based models. Hence, the rounded phone units are the most suitable acoustic units to develop Chaha ASR systems.
{"title":"Investigation of Automatic Speech Recognition Systems via the Multilingual Deep Neural Network Modeling Methods for a Very Low-Resource Language, Chaha","authors":"Tessfu Geteye Fantaye, Junqing Yu, Tulu Tilahun Hailu","doi":"10.4236/jsip.2020.111001","DOIUrl":"https://doi.org/10.4236/jsip.2020.111001","url":null,"abstract":"Automatic speech recognition (ASR) is vital for very low-resource languages for mitigating the extinction trouble. Chaha is one of the low-resource languages, which suffers from the problem of resource insufficiency and some of its phonological, morphological, and orthographic features challenge the development and initiatives in the area of ASR. By considering these challenges, this study is the first endeavor, which analyzed the characteristics of the language, prepared speech corpus, and developed different ASR systems. A small 3-hour read speech corpus was prepared and transcribed. Different basic and rounded phone unit-based speech recognizers were explored using multilingual deep neural network (DNN) modeling methods. The experimental results demonstrated that all the basic phone and rounded phone unit-based multilingual models outperformed the corresponding unilingual models with the relative performance improvements of 5.47% to 19.87% and 5.74% to 16.77%, respectively. The rounded phone unit-based multilingual models outperformed the equivalent basic phone unit-based models with relative performance improvements of 0.95% to 4.98%. Overall, we discovered that multilingual DNN modeling methods are profoundly effective to develop Chaha speech recognizers. Both the basic and rounded phone acoustic units are convenient to build Chaha ASR system. However, the rounded phone unit-based models are superior in performance and faster in recognition speed over the corresponding basic phone unit-based models. Hence, the rounded phone units are the most suitable acoustic units to develop Chaha ASR systems.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83851324","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 : 2019-11-29DOI: 10.4236/jsip.2019.104011
Wenying Ge
Shadow detection is a crucial task in high-resolution remote-sensing image processing. Various shadow detection methods have been explored during the last decades. These methods did improve the detection accuracy but are still not robust enough to get satisfactory results for failing to extract enough information from the original images. To take full advantage of various features of shadows, a new method combining edges information with the spectral and spatial information is proposed in this paper. As known, edge is one of the most important characteristics in the high-resolution remote-sensing images. Unfortunately, in shadow detection, it is a high-risk strategy to determine whether a pixel is the edge or not strictly because intensity values on shadow boundaries are always between those in shadow and non-shadow areas. Therefore, a soft edge description model is developed to describe the degree of each pixel belonging to the edges or not. Sequentially, the soft edge description is incorporating to a fuzzy clustering procedure based on HMRF (Hidden Markov Random Fields), in which more appropriate spatial contextual information can be used. More concretely, it consists of two components: the soft edge description model and an iterative shadow detection algorithm. Experiments on several remote sensing images have shown that the proposed method can obtain more accurate shadow detection results.
{"title":"Shadow Detection Method Based on HMRF with Soft Edges for High-Resolution Remote-Sensing Images","authors":"Wenying Ge","doi":"10.4236/jsip.2019.104011","DOIUrl":"https://doi.org/10.4236/jsip.2019.104011","url":null,"abstract":"Shadow detection is a crucial task in high-resolution remote-sensing image processing. Various shadow detection methods have been explored during the last decades. These methods did improve the detection accuracy but are still not robust enough to get satisfactory results for failing to extract enough information from the original images. To take full advantage of various features of shadows, a new method combining edges information with the spectral and spatial information is proposed in this paper. As known, edge is one of the most important characteristics in the high-resolution remote-sensing images. Unfortunately, in shadow detection, it is a high-risk strategy to determine whether a pixel is the edge or not strictly because intensity values on shadow boundaries are always between those in shadow and non-shadow areas. Therefore, a soft edge description model is developed to describe the degree of each pixel belonging to the edges or not. Sequentially, the soft edge description is incorporating to a fuzzy clustering procedure based on HMRF (Hidden Markov Random Fields), in which more appropriate spatial contextual information can be used. More concretely, it consists of two components: the soft edge description model and an iterative shadow detection algorithm. Experiments on several remote sensing images have shown that the proposed method can obtain more accurate shadow detection results.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80589352","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 : 2019-11-28DOI: 10.4236/jsip.2019.104009
C. Kwan, Jude Larkin
The two mast cameras, Mastcams, onboard Mars rover Curiosity are multispectral imagers with nine bands in each. Currently, the images are compressed losslessly using JPEG, which can achieve only two to three times of compression. We present a comparative study of four approaches to compressing multispectral Mastcam images. The first approach is to divide the nine bands into three groups with each group having three bands. Since the multispectral bands have strong correlation, we treat the three groups of images as video frames. We call this approach the Video approach. The second approach is to compress each group separately and we call it the split band (SB) approach. The third one is to apply a two-step approach in which the first step uses principal component analysis (PCA) to compress a nine-band image cube to six bands and a second step compresses the six PCA bands using conventional codecs. The fourth one is to apply PCA only. In addition, we also present subjective and objective assessment results for compressing RGB images because RGB images have been used for stereo and disparity map generation. Five well-known compression codecs, including JPEG, JPEG-2000 (J2K), X264, X265, and Daala in the literature, have been applied and compared in each approach. The performance of different algorithms was assessed using four well-known performance metrics. Two are conventional and another two are known to have good correlation with human perception. Extensive experiments using actual Mastcam images have been performed to demonstrate the various approaches. We observed that perceptually lossless compression can be achieved at 10:1 compression ratio. In particular, the performance gain of the SB approach with Daala is at least 5 dBs in terms peak signal-to-noise ratio (PSNR) at 10:1 compression ratio over that of JPEG. Subjective comparisons also corroborated with the objective metrics in that perceptually lossless compression can be achieved even at 20 to 1 compression.
{"title":"Perceptually Lossless Compression for Mastcam Multispectral Images: A Comparative Study","authors":"C. Kwan, Jude Larkin","doi":"10.4236/jsip.2019.104009","DOIUrl":"https://doi.org/10.4236/jsip.2019.104009","url":null,"abstract":"The two mast cameras, Mastcams, onboard Mars rover Curiosity are multispectral imagers with nine bands in each. Currently, the images are compressed losslessly using JPEG, which can achieve only two to three times of compression. We present a comparative study of four approaches to compressing multispectral Mastcam images. The first approach is to divide the nine bands into three groups with each group having three bands. Since the multispectral bands have strong correlation, we treat the three groups of images as video frames. We call this approach the Video approach. The second approach is to compress each group separately and we call it the split band (SB) approach. The third one is to apply a two-step approach in which the first step uses principal component analysis (PCA) to compress a nine-band image cube to six bands and a second step compresses the six PCA bands using conventional codecs. The fourth one is to apply PCA only. In addition, we also present subjective and objective assessment results for compressing RGB images because RGB images have been used for stereo and disparity map generation. Five well-known compression codecs, including JPEG, JPEG-2000 (J2K), X264, X265, and Daala in the literature, have been applied and compared in each approach. The performance of different algorithms was assessed using four well-known performance metrics. Two are conventional and another two are known to have good correlation with human perception. Extensive experiments using actual Mastcam images have been performed to demonstrate the various approaches. We observed that perceptually lossless compression can be achieved at 10:1 compression ratio. In particular, the performance gain of the SB approach with Daala is at least 5 dBs in terms peak signal-to-noise ratio (PSNR) at 10:1 compression ratio over that of JPEG. Subjective comparisons also corroborated with the objective metrics in that perceptually lossless compression can be achieved even at 20 to 1 compression.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86489407","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 : 2019-11-13DOI: 10.4236/jsip.2019.104010
C. Kwan, Bryan Chou, Jonathan Yang, T. Tran
Although compressive measurements save data storage and bandwidth usage, they are difficult to be used directly for target tracking and classification without pixel reconstruction. This is because the Gaussian random matrix destroys the target location information in the original video frames. This paper summarizes our research effort on target tracking and classification directly in the compressive measurement domain. We focus on one particular type of compressive measurement using pixel subsampling. That is, original pixels in video frames are randomly subsampled. Even in such a special compressive sensing setting, conventional trackers do not work in a satisfactory manner. We propose a deep learning approach that integrates YOLO (You Only Look Once) and ResNet (residual network) for multiple target tracking and classification. YOLO is used for multiple target tracking and ResNet is for target classification. Extensive experiments using short wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) videos demonstrated the efficacy of the proposed approach even though the training data are very scarce.
压缩测量虽然节省了数据存储和带宽使用，但如果不进行像素重建，则难以直接用于目标跟踪和分类。这是因为高斯随机矩阵破坏了原始视频帧中的目标位置信息。本文总结了压缩测量领域中目标跟踪与分类的研究成果。我们专注于使用像素子采样的一种特殊类型的压缩测量。也就是说，视频帧中的原始像素被随机抽样。即使在这种特殊的压缩感知设置中，传统的跟踪器也不能以令人满意的方式工作。我们提出了一种集成YOLO (You Only Look Once)和ResNet(残差网络)的深度学习方法，用于多目标跟踪和分类。YOLO用于多目标跟踪，ResNet用于目标分类。大量使用短波红外(SWIR)、中波红外(MWIR)和长波红外(LWIR)视频的实验证明了该方法的有效性，尽管训练数据非常稀缺。
{"title":"Deep Learning Based Target Tracking and Classification for Infrared Videos Using Compressive Measurements","authors":"C. Kwan, Bryan Chou, Jonathan Yang, T. Tran","doi":"10.4236/jsip.2019.104010","DOIUrl":"https://doi.org/10.4236/jsip.2019.104010","url":null,"abstract":"Although compressive measurements save data storage \u0000and bandwidth usage, they are difficult to be used directly for target tracking \u0000and classification without pixel reconstruction. This is because the Gaussian \u0000random matrix destroys the target location information in the original video \u0000frames. This paper summarizes our research effort on target tracking and \u0000classification directly in the compressive measurement domain. We focus on one \u0000particular type of compressive measurement using pixel subsampling. That is, \u0000original pixels in video frames are randomly subsampled. Even in such a special \u0000compressive sensing setting, conventional trackers do not work in a \u0000satisfactory manner. We propose a deep learning approach that integrates YOLO \u0000(You Only Look Once) and ResNet (residual network) for multiple target tracking \u0000and classification. YOLO is used for multiple target tracking and ResNet is for \u0000target classification. Extensive experiments using short wave infrared (SWIR), \u0000mid-wave infrared (MWIR), and long-wave infrared (LWIR) videos demonstrated the \u0000efficacy of the proposed approach even though the training data are very \u0000scarce.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78890066","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 : 2019-11-13DOI: 10.4236/jsip.2019.104008
Manzar Mahmud, Wilson Q. Wang
Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed in this paper for rotor bar fault detection in IMs. As the characteristic fault frequency will change with operating conditions related to load and speed, the proposed adaptive EMD technique correlates fault features over different frequency bands and intrinsic mode function (IMF) sidebands. The adaptive EMD technique uses the first IMF to detect the fault type and the second IMF as an indicator to predict the fault severity. It can overcome the problems of the sensitivity of sideband frequencies related to the speed and load oscillations. The effectiveness of the proposed adaptive EMD technique is verified by experimental tests under different motor conditions.
{"title":"An Adaptive EMD Technique for Induction Motor Fault Detection","authors":"Manzar Mahmud, Wilson Q. Wang","doi":"10.4236/jsip.2019.104008","DOIUrl":"https://doi.org/10.4236/jsip.2019.104008","url":null,"abstract":"Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed in this paper for rotor bar fault detection in IMs. As the characteristic fault frequency will change with operating conditions related to load and speed, the proposed adaptive EMD technique correlates fault features over different frequency bands and intrinsic mode function (IMF) sidebands. The adaptive EMD technique uses the first IMF to detect the fault type and the second IMF as an indicator to predict the fault severity. It can overcome the problems of the sensitivity of sideband frequencies related to the speed and load oscillations. The effectiveness of the proposed adaptive EMD technique is verified by experimental tests under different motor conditions.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79725448","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 : 2019-08-26DOI: 10.4236/JSIP.2019.103007
C. Kwan, Jude Larkin
Hyperspectral images (HSI) have hundreds of bands, which impose heavy burden on data storage and transmission bandwidth. Quite a few compression techniques have been explored for HSI in the past decades. One high performing technique is the combination of principal component analysis (PCA) and JPEG-2000 (J2K). However, since there are several new compression codecs developed after J2K in the past 15 years, it is worthwhile to revisit this research area and investigate if there are better techniques for HSI compression. In this paper, we present some new results in HSI compression. We aim at perceptually lossless compression of HSI. Perceptually lossless means that the decompressed HSI data cube has a performance metric near 40 dBs in terms of peak-signal-to-noise ratio (PSNR) or human visual system (HVS) based metrics. The key idea is to compare several combinations of PCA and video/ image codecs. Three representative HSI data cubes were used in our studies. Four video/image codecs, including J2K, X264, X265, and Daala, have been investigated and four performance metrics were used in our comparative studies. Moreover, some alternative techniques such as video, split band, and PCA only approaches were also compared. It was observed that the combination of PCA and X264 yielded the best performance in terms of compression performance and computational complexity. In some cases, the PCA + X264 combination achieved more than 3 dBs than the PCA + J2K combination.
{"title":"New Results in Perceptually Lossless Compression of Hyperspectral Images","authors":"C. Kwan, Jude Larkin","doi":"10.4236/JSIP.2019.103007","DOIUrl":"https://doi.org/10.4236/JSIP.2019.103007","url":null,"abstract":"Hyperspectral images (HSI) have hundreds of bands, which impose heavy \u0000burden on data storage and transmission bandwidth. Quite a few compression \u0000techniques have been explored for HSI in the past decades. One high performing \u0000technique is the combination of principal component analysis (PCA) \u0000and JPEG-2000 (J2K). However, since there are several new compression codecs \u0000developed after J2K in the past 15 years, it is worthwhile to revisit this research \u0000area and investigate if there are better techniques for HSI compression. \u0000In this paper, we present some new results in HSI compression. We aim at \u0000perceptually lossless compression of HSI. Perceptually lossless means that the \u0000decompressed HSI data cube has a performance metric near 40 dBs in terms of \u0000peak-signal-to-noise ratio (PSNR) or human visual system (HVS) based metrics. \u0000The key idea is to compare several combinations of PCA and video/ \u0000image codecs. Three representative HSI data cubes were used in our studies. \u0000Four video/image codecs, including J2K, X264, X265, and Daala, have \u0000been investigated and four performance metrics were used in our comparative \u0000studies. Moreover, some alternative techniques such as video, split band, and \u0000PCA only approaches were also compared. It was observed that the combination \u0000of PCA and X264 yielded the best performance in terms of compression \u0000performance and computational complexity. In some cases, the PCA + X264 \u0000combination achieved more than 3 dBs than the PCA + J2K combination.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74762913","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 : 2019-08-08DOI: 10.4236/JSIP.2019.103005
M. R. Souza, H. Pedrini
Image compression techniques aim to reduce redundant information in order to allow data storage and transmission in an efficient way. In this work, we propose and analyze a lossy image compression method based on the singular value decomposition using an optimal choice of eigenvalues and an adaptive mechanism for block partitioning. Experiments are conducted on several images to demonstrate the effectiveness of the proposed compression method in comparison with the direct application of the singular value decomposition.
{"title":"Adaptive Lossy Image Compression Based on Singular Value Decomposition","authors":"M. R. Souza, H. Pedrini","doi":"10.4236/JSIP.2019.103005","DOIUrl":"https://doi.org/10.4236/JSIP.2019.103005","url":null,"abstract":"Image compression techniques aim to reduce redundant information in order to allow data storage and transmission in an efficient way. In this work, we propose and analyze a lossy image compression method based on the singular value decomposition using an optimal choice of eigenvalues and an adaptive mechanism for block partitioning. Experiments are conducted on several images to demonstrate the effectiveness of the proposed compression method in comparison with the direct application of the singular value decomposition.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79140126","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 : 2019-08-08DOI: 10.4236/JSIP.2019.103006
C. Kwan, Bryan Chou, Jonathan Yang, Akshay Rangamani, T. Tran, Jack Zhang, R. Etienne-Cummings
Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing camera that has low power consumption and high compression ratio. Moreover, a PCE camera can control individual pixel exposure time that can enable high dynamic range. Conventional approaches of using PCE camera involve a time consuming and lossy process to reconstruct the original frames and then use those frames for target tracking and classification. In this paper, we present a deep learning approach that directly performs target tracking and classification in the compressive measurement domain without any frame reconstruction. Our approach has two parts: tracking and classification. The tracking has been done using YOLO (You Only Look Once) and the classification is achieved using Residual Network (ResNet). Extensive experiments using mid-wave infrared (MWIR) and long-wave infrared (LWIR) videos demonstrated the efficacy of our proposed approach.
{"title":"Target Tracking and Classification Using Compressive Measurements of MWIR and LWIR Coded Aperture Cameras","authors":"C. Kwan, Bryan Chou, Jonathan Yang, Akshay Rangamani, T. Tran, Jack Zhang, R. Etienne-Cummings","doi":"10.4236/JSIP.2019.103006","DOIUrl":"https://doi.org/10.4236/JSIP.2019.103006","url":null,"abstract":"Pixel-wise Code Exposure (PCE) camera is one type of compressive sensing \u0000camera that has low power consumption and high compression ratio. Moreover, \u0000a PCE camera can control individual pixel exposure time that can enable \u0000high dynamic range. Conventional approaches of using PCE camera involve \u0000a time consuming and lossy process to reconstruct the original frames \u0000and then use those frames for target tracking and classification. In this paper, \u0000we present a deep learning approach that directly performs target tracking \u0000and classification in the compressive measurement domain without any \u0000frame reconstruction. Our approach has two parts: tracking and classification. \u0000The tracking has been done using YOLO (You Only Look Once) and the \u0000classification is achieved using Residual Network (ResNet). Extensive experiments \u0000using mid-wave infrared (MWIR) and long-wave infrared (LWIR) \u0000videos demonstrated the efficacy of our proposed approach.","PeriodicalId":38474,"journal":{"name":"Journal of Information Hiding and Multimedia Signal Processing","volume":"231 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76109071","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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