Open AccessAn efficient post-processing adaptive filtering technique to rectifying the flickering effects
Author(s) -
Anudeep Gandam,
Jagroop Singh Sidhu,
Sahil Verma,
N. Z. Jhanjhi,
Anand Nayyar,
Mohamed Abouhawwash,
Yunyoung Nam
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0250959
Subject(s) - ringing artifacts , ringing , flicker , computer science , artificial intelligence , computer vision , decoding methods , compression artifact , quantization (signal processing) , video quality , artifact (error) , filter (signal processing) , image processing , pattern recognition (psychology) , algorithm , image compression , image (mathematics) , computer graphics (images) , engineering , metric (unit) , operations management
Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. The PP-AFT method differentiates the blocked images or frames using activity function into different regions and developed adaptive filters as per the classified region. The designed process also introduces an adaptive flicker extraction and removal method and a 2-D filter to remove ringing effects in edge regions. The PP-AFT technique is implemented on various videos, and results are compared with different existing techniques using performance metrics like PSNR-B, MSSIM, and GBIM. Simulation results show significant improvement in the subjective quality of different video frames. The proposed method outperforms state-of-the-art de-blocking methods in terms of PSNR-B with average value lying between (0.7–1.9db) while (35.83–47.7%) reduced average GBIM keeping MSSIM values very close to the original sequence statistically 0.978.