Development of modal own embedding type reduction image codebook and highly magnifying image expansion based on fuzzy inference

2014 Fiscal Year Final Research Report

• Project/Area Number: 24300092
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Partial Multi-year Fund
• Section: 一般
• Research Field: Sensitivity informatics/Soft computing
• Research Institution: Kyushu Institute of Information Sciences
• Principal Investigator: ASO Takashi 九州情報大学, 経営情報学部, 教授 (20259683)
• Co-Investigator(Kenkyū-buntansha): CHA Byungki 九州情報大学, 経営情報学部, 教授 (10310004) ; SUETAKE Noriaki 山口大学, 大学院理工学研究科, 准教授 (80334051) ; KAWANO Hideaki 九州工業大学, 大学院工学研究院電気電子工学研究系, 准教授 (00404096) ; TAMUKOH Hakaru 九州工業大学, 大学院生命体工学研究科, 准教授 (90432955)
• Research Collaborator: KUBOTA Ryosuke
• Project Period (FY): 2012-04-01 – 2015-03-31
• Keywords: ファジィ推論 / 映像拡大処理 / コードブック

Outline of Final Research Achievements

While many displays are higher resolution, the measure increases in storage of a high resolution image and a physical load of transmission, and is urgently needed. This research proposes the new framework to get a high resolution image from a little low resolution image of preservation and a transmission load by highly magnifying expansion. When premising that there is a high resolution image in hand and changing a high resolution image to a low resolution image by this structure, when magnifying the ingredient expansion processing can't restore with to be embedded, it's compensated by read information. It's preliminary to the reduction image which becomes a starting point by the codebook image expansion way to which the fuzzy inference that we have developed it so far was applied. High quality of image quality in the highly magnifying image expansion which was to do a device, and was difficult up to was achieved.

Free Research Field

総合領域