Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Advance Sustainable Science, Engineering and Technology (ASSET)
Ali Abdulazeez Mohammed Baqer Qazzaz
University of Kufa
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Unifying Dual-Pyramid Structure and Y–G Channel Synergy for Full-Reference Image Quality Assessment Ali Abdulazeez Mohammed Baqer Qazzaz; Yousif Samer Mudhafar; Siraj Muneer Mahboba
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2783

Abstract

Standard metrics such as SSIM often overlook complex chromatic distortions, creating a gap between objective scores and human judgment. To address this, we present the Synergistic Structural Similarity Index (SSSI), a metric grounded in a novel dual-pyramid strategy that integrates Gaussian-blurred stability with direct subsampling sharpness. Our method departs from luminance-only analysis by employing an equal, synergistic partnership between the luminance (Y) and Green (G) channels, mirroring the eye's spectral sensitivity. On the KADID-10k dataset, SSSI achieves an SROCC of 0.7793. This represents a significant 4% performance gain over the standard SSIM baseline, demonstrating that integrating chromatic data with dual-scale structural analysis provides a more accurate proxy for human visual perception.
A multi-expert approach to content-based image retrieval using feature fusion and late re-ranking Ali Abdulazeez Mohammed Baqer Qazzaz; Yousif Samer Mudhafar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1376-1384

Abstract

As digital data rapidly grows, content-based image retrieval (CBIR) has become important for optimizing collections of visual data. This work proposes a retrieval framework which operates in two stages and improves accuracy by using systematic fusion of features. In the first stage, first-stage wide-scope descriptors called bag-of-visual-words (BoVW), scattering wavelet transform (SWT), discrete cosine transform (DCT), and principal component analysis (PCA) retrieve initial candidate images. The second stage undertakes detailed re-ordering of candidate images by implementing the local binary pattern (LBP), histogram of oriented gradients (HOG), and singular value decomposition (SVD) descriptors to re-evaluate similarity scores. Each individual descriptor returned results for mean average precision for the top 10 retrieved images (mAP, top-10) of between 0.63 and 0.79 and the fused framework achieved 0.88, which is evidence of the viability of complementary feature integration. These findings support the hypothesis that while multiple descriptors performed well and delivered high retrieval accuracy, hierarchical fusion of multiple handcrafted descriptors does not involve the computational costs associated with deep learning methods.
Co-Authors Siraj Muneer Mahboba Yousif Samer Mudhafar Yousif Samer Mudhafar