{"title":"视频编码中基于数组的可扩展DCT计算体系结构","authors":"Jian Huang, Jooheung Lee, Yimin Ge","doi":"10.1109/ICNNSP.2008.4590391","DOIUrl":null,"url":null,"abstract":"In this paper, we propose an array-based architecture for DCT computation with high scalability. The scalable architecture can perform DCT computations for 15 different zones and 8 different precisions to achieve quality scalability for DCT coefficients. Due to the quantization process in video coding, the quality can still be retained for larger quantization parameter. We show the detailed comparisons between the quality scalability and the tradeoff factors, i.e., throughput, hardware resources, clock frequencies, and power consumptions.","PeriodicalId":250993,"journal":{"name":"2008 International Conference on Neural Networks and Signal Processing","volume":"198 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"An array-based scalable architecture for DCT computations in video coding\",\"authors\":\"Jian Huang, Jooheung Lee, Yimin Ge\",\"doi\":\"10.1109/ICNNSP.2008.4590391\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose an array-based architecture for DCT computation with high scalability. The scalable architecture can perform DCT computations for 15 different zones and 8 different precisions to achieve quality scalability for DCT coefficients. Due to the quantization process in video coding, the quality can still be retained for larger quantization parameter. We show the detailed comparisons between the quality scalability and the tradeoff factors, i.e., throughput, hardware resources, clock frequencies, and power consumptions.\",\"PeriodicalId\":250993,\"journal\":{\"name\":\"2008 International Conference on Neural Networks and Signal Processing\",\"volume\":\"198 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Neural Networks and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNNSP.2008.4590391\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Neural Networks and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNNSP.2008.4590391","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An array-based scalable architecture for DCT computations in video coding
In this paper, we propose an array-based architecture for DCT computation with high scalability. The scalable architecture can perform DCT computations for 15 different zones and 8 different precisions to achieve quality scalability for DCT coefficients. Due to the quantization process in video coding, the quality can still be retained for larger quantization parameter. We show the detailed comparisons between the quality scalability and the tradeoff factors, i.e., throughput, hardware resources, clock frequencies, and power consumptions.