{"title":"基于离散余弦变换的金融数字图像压缩方法","authors":"Wenjin Wang, Miaomiao Lu, Xuanling Dai, Ping Jiang","doi":"10.3103/S014641162470069X","DOIUrl":null,"url":null,"abstract":"<p>In response to the characteristics of financial image data, this paper proposes an efficient digital image compression scheme. Firstly, discrete cosine transform (DCT) is applied to divide the financial image into DC and AC coefficients. Secondly, based on the characteristics of DCT coefficients, a fuzzy method is employed to categorize DCT subblocks into smooth, texture, and edge classes, enabling distinct quantization strategies. Subsequently, to eliminate spatial and statistical redundancies in financial images, common features and structures are utilized, and a specific scanning approach is employed to optimize the arrangement of important coefficients. Finally, differential prediction and entropy coding are employed for DCT coefficient scanning encoding, enhancing compression efficiency. The objective evaluation metrics of this algorithm are approximately 2 dB higher than existing algorithms at bit rates of 0.25 and 0.5. Even at bit rates of 0.75, 1.5, 2.5, and 3.5, the performance of this method still outperforms the comparative algorithms, demonstrating its capability to efficiently store and transmit massive financial image data, thereby providing robust support for data processing in the financial sector.</p>","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"58 5","pages":"592 - 601"},"PeriodicalIF":0.6000,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Financial Digital Images Compression Method Based on Discrete Cosine Transform\",\"authors\":\"Wenjin Wang, Miaomiao Lu, Xuanling Dai, Ping Jiang\",\"doi\":\"10.3103/S014641162470069X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In response to the characteristics of financial image data, this paper proposes an efficient digital image compression scheme. Firstly, discrete cosine transform (DCT) is applied to divide the financial image into DC and AC coefficients. Secondly, based on the characteristics of DCT coefficients, a fuzzy method is employed to categorize DCT subblocks into smooth, texture, and edge classes, enabling distinct quantization strategies. Subsequently, to eliminate spatial and statistical redundancies in financial images, common features and structures are utilized, and a specific scanning approach is employed to optimize the arrangement of important coefficients. Finally, differential prediction and entropy coding are employed for DCT coefficient scanning encoding, enhancing compression efficiency. The objective evaluation metrics of this algorithm are approximately 2 dB higher than existing algorithms at bit rates of 0.25 and 0.5. Even at bit rates of 0.75, 1.5, 2.5, and 3.5, the performance of this method still outperforms the comparative algorithms, demonstrating its capability to efficiently store and transmit massive financial image data, thereby providing robust support for data processing in the financial sector.</p>\",\"PeriodicalId\":46238,\"journal\":{\"name\":\"AUTOMATIC CONTROL AND COMPUTER SCIENCES\",\"volume\":\"58 5\",\"pages\":\"592 - 601\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2024-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AUTOMATIC CONTROL AND COMPUTER SCIENCES\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.3103/S014641162470069X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.3103/S014641162470069X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Financial Digital Images Compression Method Based on Discrete Cosine Transform
In response to the characteristics of financial image data, this paper proposes an efficient digital image compression scheme. Firstly, discrete cosine transform (DCT) is applied to divide the financial image into DC and AC coefficients. Secondly, based on the characteristics of DCT coefficients, a fuzzy method is employed to categorize DCT subblocks into smooth, texture, and edge classes, enabling distinct quantization strategies. Subsequently, to eliminate spatial and statistical redundancies in financial images, common features and structures are utilized, and a specific scanning approach is employed to optimize the arrangement of important coefficients. Finally, differential prediction and entropy coding are employed for DCT coefficient scanning encoding, enhancing compression efficiency. The objective evaluation metrics of this algorithm are approximately 2 dB higher than existing algorithms at bit rates of 0.25 and 0.5. Even at bit rates of 0.75, 1.5, 2.5, and 3.5, the performance of this method still outperforms the comparative algorithms, demonstrating its capability to efficiently store and transmit massive financial image data, thereby providing robust support for data processing in the financial sector.
期刊介绍:
Automatic Control and Computer Sciences is a peer reviewed journal that publishes articles on• Control systems, cyber-physical system, real-time systems, robotics, smart sensors, embedded intelligence • Network information technologies, information security, statistical methods of data processing, distributed artificial intelligence, complex systems modeling, knowledge representation, processing and management • Signal and image processing, machine learning, machine perception, computer vision