{"title":"基于像素域特征的分块压缩感知图像测量编码系统","authors":"Jirayu Peetakul, Jinjia Zhou, K. Wada","doi":"10.1109/DCC.2019.00111","DOIUrl":null,"url":null,"abstract":"Compressive sensing (CS) is data acquiring and innovative mathematical approach that accelerate and efficient sampling from large into small volumes of data. Moreover, it could be dramatically reduced amounts of sensor, power consumption, storage size, and bandwidth which results in lower hardware costs [1]. In wireless cameras network for video surveillance, the large amount of data is produced. However, there is still a lot of redundant data in measurement domain. To solve this problem, coding techniques such as block-based CS (BCS), intra-prediction and quantization is applied to avoid higher rate-distortion than other CS frameworks. Therefore, new imaging architecture has been proposed to be sensed, removed redundant information, and compressed simultaneously, thus leading to the faster image acquisition system.","PeriodicalId":167723,"journal":{"name":"2019 Data Compression Conference (DCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A Measurement Coding System for Block-Based Compressive Sensing Images by Using Pixel-Domain Features\",\"authors\":\"Jirayu Peetakul, Jinjia Zhou, K. Wada\",\"doi\":\"10.1109/DCC.2019.00111\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Compressive sensing (CS) is data acquiring and innovative mathematical approach that accelerate and efficient sampling from large into small volumes of data. Moreover, it could be dramatically reduced amounts of sensor, power consumption, storage size, and bandwidth which results in lower hardware costs [1]. In wireless cameras network for video surveillance, the large amount of data is produced. However, there is still a lot of redundant data in measurement domain. To solve this problem, coding techniques such as block-based CS (BCS), intra-prediction and quantization is applied to avoid higher rate-distortion than other CS frameworks. Therefore, new imaging architecture has been proposed to be sensed, removed redundant information, and compressed simultaneously, thus leading to the faster image acquisition system.\",\"PeriodicalId\":167723,\"journal\":{\"name\":\"2019 Data Compression Conference (DCC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Data Compression Conference (DCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DCC.2019.00111\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Data Compression Conference (DCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DCC.2019.00111","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Measurement Coding System for Block-Based Compressive Sensing Images by Using Pixel-Domain Features
Compressive sensing (CS) is data acquiring and innovative mathematical approach that accelerate and efficient sampling from large into small volumes of data. Moreover, it could be dramatically reduced amounts of sensor, power consumption, storage size, and bandwidth which results in lower hardware costs [1]. In wireless cameras network for video surveillance, the large amount of data is produced. However, there is still a lot of redundant data in measurement domain. To solve this problem, coding techniques such as block-based CS (BCS), intra-prediction and quantization is applied to avoid higher rate-distortion than other CS frameworks. Therefore, new imaging architecture has been proposed to be sensed, removed redundant information, and compressed simultaneously, thus leading to the faster image acquisition system.