Hiba Hameed Alshuwaili, A. Ibrahim, Salwa A. Al-agha
{"title":"基于特征向量的医学视频场景压缩","authors":"Hiba Hameed Alshuwaili, A. Ibrahim, Salwa A. Al-agha","doi":"10.1109/HORA52670.2021.9461392","DOIUrl":null,"url":null,"abstract":"With the technological development, it has become easy to use digital data, and thus medical institutions have turned to analyzing images and video content. Medical video is recorded in order to collect experimental information about the use of technologies and integrated data that consists of a series of doctors with video recording, and this leads to user interaction with computers. Using the coding methodology, the video is analyzed for a medical diagnosis that contains important information about the patient’s condition with the help of computers, as well as performing surgeries in a medical environment to conduct clinical trials and diagnostics. This leads to software development, reduction in medical effort real-time quality measurement and utilization of available treatments for patient medical diagnosis Medical video has become one of the leading and most common fields, in addition to medical images for diagnosing heart and lung diseases, brain and digestive diseases, and this diagnosis is made by using ultrasound and magnetic resonance imaging in addition to a CT scan. All this helped to provide health care through new methods of research and experiments clinical. Lately, Compression technologies developed video and images are widely used, and by using these effective compression techniques, little or no damage is achieved, the file size is reduced and the visual quality is not affected, major target of compression is to enable data to get saved or transmit in the better form types of compression in various manners like the lossless and the lossy. The aim of this study is to compress video film to lower the irrelevance and the redundancy of data, principle component analysis (PCA) is used in this research, the main component analysis of medical video compression based on self-matrix analysis that all readers will be able to understand the PCA method. This paper presents a suitable technique for medical video compression using Principle Component Analysis PCA, compressed the frames of medical video with eigenvalue and eigenvector techniques. Experimental results shown the efficient and quick technique for medical video frames.","PeriodicalId":270469,"journal":{"name":"2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Compressing the Scenes of Medical Video by Using Eigenvector\",\"authors\":\"Hiba Hameed Alshuwaili, A. Ibrahim, Salwa A. Al-agha\",\"doi\":\"10.1109/HORA52670.2021.9461392\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the technological development, it has become easy to use digital data, and thus medical institutions have turned to analyzing images and video content. Medical video is recorded in order to collect experimental information about the use of technologies and integrated data that consists of a series of doctors with video recording, and this leads to user interaction with computers. Using the coding methodology, the video is analyzed for a medical diagnosis that contains important information about the patient’s condition with the help of computers, as well as performing surgeries in a medical environment to conduct clinical trials and diagnostics. This leads to software development, reduction in medical effort real-time quality measurement and utilization of available treatments for patient medical diagnosis Medical video has become one of the leading and most common fields, in addition to medical images for diagnosing heart and lung diseases, brain and digestive diseases, and this diagnosis is made by using ultrasound and magnetic resonance imaging in addition to a CT scan. All this helped to provide health care through new methods of research and experiments clinical. Lately, Compression technologies developed video and images are widely used, and by using these effective compression techniques, little or no damage is achieved, the file size is reduced and the visual quality is not affected, major target of compression is to enable data to get saved or transmit in the better form types of compression in various manners like the lossless and the lossy. The aim of this study is to compress video film to lower the irrelevance and the redundancy of data, principle component analysis (PCA) is used in this research, the main component analysis of medical video compression based on self-matrix analysis that all readers will be able to understand the PCA method. This paper presents a suitable technique for medical video compression using Principle Component Analysis PCA, compressed the frames of medical video with eigenvalue and eigenvector techniques. 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Compressing the Scenes of Medical Video by Using Eigenvector
With the technological development, it has become easy to use digital data, and thus medical institutions have turned to analyzing images and video content. Medical video is recorded in order to collect experimental information about the use of technologies and integrated data that consists of a series of doctors with video recording, and this leads to user interaction with computers. Using the coding methodology, the video is analyzed for a medical diagnosis that contains important information about the patient’s condition with the help of computers, as well as performing surgeries in a medical environment to conduct clinical trials and diagnostics. This leads to software development, reduction in medical effort real-time quality measurement and utilization of available treatments for patient medical diagnosis Medical video has become one of the leading and most common fields, in addition to medical images for diagnosing heart and lung diseases, brain and digestive diseases, and this diagnosis is made by using ultrasound and magnetic resonance imaging in addition to a CT scan. All this helped to provide health care through new methods of research and experiments clinical. Lately, Compression technologies developed video and images are widely used, and by using these effective compression techniques, little or no damage is achieved, the file size is reduced and the visual quality is not affected, major target of compression is to enable data to get saved or transmit in the better form types of compression in various manners like the lossless and the lossy. The aim of this study is to compress video film to lower the irrelevance and the redundancy of data, principle component analysis (PCA) is used in this research, the main component analysis of medical video compression based on self-matrix analysis that all readers will be able to understand the PCA method. This paper presents a suitable technique for medical video compression using Principle Component Analysis PCA, compressed the frames of medical video with eigenvalue and eigenvector techniques. Experimental results shown the efficient and quick technique for medical video frames.