tailieunhanh - An HVS-inspired video deinterlacer based on visual saliency

In this paper, a spatial saliency-guided motion-compensated deinterlacing method is proposed which accounts for the properties of the Human Visual System (HVS): Our algorithm classifies the field according to its texture and viewer’s region of interest and adapts the motion estimation and compensation, as well as the saliency-guided interpolation to ensure high-quality frame reconstruction. | Vietnam J Comput Sci 2017 4 61-69 DOI s40595-016-0081-1 CrossMark REGULAR PAPER An HVS-inspired video deinterlacer based on visual saliency Umang Aggarwal 1 Maria Trocan1 Francois-Xavier Coudoux2 Received 14 May 2016 Accepted 29 August 2016 Published online 16 September 2016 The Author s 2016. This article is published with open access at Abstract Video deinterlacing is a technique wherein the interlaced video format is converted into progressive scan format for nowadays display devices. In this paper a spatial saliency-guided motion-compensated deinterlacing method is proposed which accounts for the properties of the Human Visual System HVS our algorithm classifies the field according to its texture and viewer s region of interest and adapts the motion estimation and compensation as well as the saliency-guided interpolation to ensure high-quality frame reconstruction. Two different saliency models namely the graph-based visual saliency GBVS model and the spectral residual visual saliency SRVS model have been studied and compared in terms of visual quality performances as well as computational complexity. The experimental results on a great variety of video test sequences show significant improvement of reconstructed video quality with the GBVS-based proposed method compared to classical motion-compensated and adaptive deinterlacing techniques with up to dB gains in terms of PSNR. Simulations also show that the SRVS-based deinterlacing process can result to significant reductions of complexity up to 25 times a decrease of the computation time compared with the GBVS-based method at the expense of a PSNR decrease. Keywords Deinterlacing Visual saliency Human visual system HVS Video quality B Maria Trocan Francois-Xavier Coudoux 1 Institut Superieur d Electronique de Paris 28 Rue Notre Dame des Champs Paris France 2 IEMN UMR CNRS 8520 Department OAE Valenciennes University .

• Quản trị mạng
• Visual saliency
• Human visual system
• Video quality
• Graph based visual saliency
• Spectral residual visual saliency
• Journal of Automation and Control Engineering
• Adaptive probabilistic tracking
• Visual saliency selection reliable particles
• Large illumination variation
• Non target with similar features
• Báo cáo sinh học hay
• cách trình bày báo cáo
• báo cáo sinh học
• công trình nghiên cứu sinh học
• tài liệu về sinh học
• Phân vùng đối tượng video
• Nhận diện vật thể nổi bật
• Nhận diện vật thể
• Bộ dữ liệu DAVIS 2016
• Phân vùng ngữ nghĩa