Muhammad Shahid

695 citations
37 papers · 430 indexed · h-index 12

Muhammad Shahid

34 papers receiving 408 citations

Peers

Muhammad Shahid
Comparison fields: 5 of 70
  • Computer Vision and Pattern Recognition 345
  • Signal Processing 171
  • Media Technology 70
  • Urban Studies 10
  • Artificial Intelligence 42
Replace Snježana Rimac-Drlje with:
Snježana Rimac-Drlje Croatia
Matteo Naccari United Kingdom
Christos G. Bampis United States
Heydi Méndez-Vázquez Mexico
Stéphane Coulombe Canada
Chao Lan United States
Jingning Han United States
Xiaochen Lian China
Muhammad Shahid relative to Snježana Rimac-Drlje Croatia Snježana Rimac-Drlje's profile →
Citations per field
00.5×2.7×
Snježana Rimac-Drlje · 1×
Citations per year

Countries citing papers authored by Muhammad Shahid

Since Specialization
Citations

This map shows the geographic impact of Muhammad Shahid's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Muhammad Shahid with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muhammad Shahid more than expected).

Fields of papers citing papers by Muhammad Shahid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Muhammad Shahid. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Muhammad Shahid. The network helps show where Muhammad Shahid may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Muhammad Shahid, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Muhammad Shahid Line = papers co-authored together Muhammad Shahid links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20251
3 20231
4 202228
5 20193
6 201610
7 20166
8 20162
9 201625
10 201515
11 20150
12
Methods for Objective and Subjective Video Quality Assessment and for Speech Enhancement
20141
13 20148
14 2014123
15 20135
16 201319
17 20138
18 20122
19 20111
20 20116

About Muhammad Shahid

Muhammad Shahid is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Media Technology, having authored 37 papers that have together received 430 indexed citations. Recurring topics across this work include Image and Video Quality Assessment (21 papers), Video Coding and Compression Technologies (16 papers), Advanced Data Compression Techniques (10 papers), Advanced Image Processing Techniques (10 papers), Image and Signal Denoising Methods (4 papers), Visual Attention and Saliency Detection (4 papers), Advanced Vision and Imaging (4 papers) and Speech and Audio Processing (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (345 citations), Signal Processing (171 citations) and Media Technology (70 citations). Muhammad Shahid has collaborated with scholars based in Sweden, Pakistan and Spain. Frequent co-authors include Benny Lövström, Hans‐Jürgen Zepernick, Ashfaq Ahmed, Kjell Brunnström, Ata Ur Rehman, Muhammad Gufran Khan, Muhammad Arslan Usman, Soo Young Shin, Kun Wang and Lisimachos P. Kondi. Their work appears in journals such as IEEE Transactions on Circuits and Systems for Video Technology, Applied Sciences and Artificial Intelligence Review.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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