{"title":"Controlled hardware architecture for fractal image compression","authors":"M. K. Naskar, N. A. Hasanujjaman, U. Biswas","doi":"10.1504/ijnbm.2020.10029634","DOIUrl":null,"url":null,"abstract":"Fractal image compression utilising algorithms have a high demand on the memory interface and the processor's arithmetic unit, which in turn fails to utilise the full capabilities of a general purpose processor. Since the algorithm is repetitive, the parallelisation reduces the time complexity of the otherwise expensive coding scheme. The design for FIC is proposed in this paper. It is based on the fact that the algorithm requires only integer arithmetic with repetitive use of the same data set. Making use of multiple functional units, controlled parallelism is introduced in this process. This makes encoding time 80 times faster than high level software implementation. It is 25 times faster than the assembly level implementation on a DSP processor.","PeriodicalId":13999,"journal":{"name":"International Journal of Nano and Biomaterials","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Nano and Biomaterials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijnbm.2020.10029634","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Chemistry","Score":null,"Total":0}
引用次数: 1
Abstract
Fractal image compression utilising algorithms have a high demand on the memory interface and the processor's arithmetic unit, which in turn fails to utilise the full capabilities of a general purpose processor. Since the algorithm is repetitive, the parallelisation reduces the time complexity of the otherwise expensive coding scheme. The design for FIC is proposed in this paper. It is based on the fact that the algorithm requires only integer arithmetic with repetitive use of the same data set. Making use of multiple functional units, controlled parallelism is introduced in this process. This makes encoding time 80 times faster than high level software implementation. It is 25 times faster than the assembly level implementation on a DSP processor.
期刊介绍:
In recent years, frontiers of research in engineering, science and technology have been driven by developments in nanomaterials, encompassing a diverse range of disciplines such as materials science, biomedical engineering, nanomedicine and biology, manufacturing technology, biotechnology, nanotechnology, and nanoelectronics. IJNBM provides an interdisciplinary vehicle covering these fields. Advanced materials inspired by biological systems and processes are likely to influence the development of novel technologies for a wide variety of applications from vaccines to artificial tissues and organs to quantum computers. Topics covered include Nanostructured materials/surfaces/interfaces Synthesis of nanostructures Biological/biomedical materials Artificial organs/tissues Tissue engineering Bioengineering materials Medical devices Functional/structural nanomaterials Carbon-based materials Nanomaterials characterisation Novel applications of nanomaterials Modelling of behaviour of nanomaterials Nanomaterials for biomedical applications Biological response to nanomaterials.