Open AccessA New Rapid and Detail-Preserving Image Downscaling Without Noise Amplification
Author(s) -
Su Hyon Kim,
Thae Song Kim
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3212060
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Video web portals have to downscale an incredible number of videos and images to various resolutions each day. The latest image downscaling algorithms conserve edges and fine details but still suffer from noise amplification. This often makes undesirable artifacts especially with a large downscaling factor. Deep learning based methods show superior performance only for some predetermined integer factors. For a practical real-time image processing that meets the requirement of video portals, it is still challenging to scale image down as quickly as possible without emphasizing noise, preserving sharp edges and important details with an arbitrary non-integer and even a large factor. In this paper, we propose a new detail-preserving image downscaling algorithm based on inverse joint bilateral filtering using an area pixel model and moving average. The proposed method not only retains visually important details but also prevents unpleasant noise amplification and aliasing artifacts by smoothing filter coefficients with respect to the downscaling factor through two-step one-dimensional filtering. Experimental results show that the proposed algorithm is about 7.37% faster on average than the fastest existing detail-preserving image downscaler. GPU implementation of our algorithm downscales a 2K video to 128-pixel width without temporal artifacts at speed of 116 frames per second. Moreover, the PSNR and SSIM scores achieved by our method were respectively 35.9% and 16.5% higher on average than the highest values scored by the existing methods when downscaling images contaminated by 5% salt and pepper noise.