2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDIVMSP-24.5
Paper Title REAL VERSUS FAKE 4K - AUTHENTIC RESOLUTION ASSESSMENT
Authors Rishi Rajesh Shah, Vyas Anirudh Akundy, Zhou Wang, University of Waterloo, Canada
SessionIVMSP-24: Applications 2
LocationGather.Town
Session Time:Thursday, 10 June, 15:30 - 16:15
Presentation Time:Thursday, 10 June, 15:30 - 16:15
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVSMR] Image & Video Sensing, Modeling, and Representation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract In recent years, the native 4K/Ultra High Definition (UHD) resolution has been trending towards the new normal of video content creation and distribution, but the practical pipelines of video acquisition, production and delivery often involve downscaling stages where the spatial resolution drops below the 4K level. Even though the video may be upscaled back to 4K/UHD resolution later, the content has lost its authentic resolution. This work aims at authentic resolution assessment (ARA). We first construct a database of over 10,000 real and fake 4K/UHD images. We then develop a two-stage ARA (TSARA) approach that classifies a video frame to have real or fake 4K resolution, where the first stage classifies local patches using a convolutional neural network (CNN), and the second stage aggregates local assessment into a global image level decision using logistical regression. Experimental results show that the proposed approach achieves high accuracy at low computational cost, and outperforms state-of-the-art no-reference (NR) image quality assessment (IQA) and image sharpness assessment (ISA) models. The built database and the proposed method are made publicly available1.