| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 29 | 751 | 1958 | 78 |
| Electronics and Electrical Engineering | 105 | 97 | 189 | 30 |
IEEE Transactions on Circuits and Systems for Video Technology mainly tackles studies in Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Data compression. Presentations on Artificial intelligence include those discussing Feature extraction, Image processing, Motion estimation, Motion compensation and Pixel. It focuses on Feature extraction but the discussions also offer insight into other areas such as Visualization and Machine learning.
The journal facilitates discussions on Motion compensation that incorporate concepts from other fields like Multiview Video Coding, Coding tree unit and Block-matching algorithm. The research on Multiview Video Coding tackled can also make contributions to studies in the areas of Scalable Video Coding and Context-adaptive binary arithmetic coding. Image segmentation, Video tracking, Image quality, Segmentation and Object detection are all areas of Computer vision tackled in IEEE Transactions on Circuits and Systems for Video Technology.
Topics in Algorithm were tackled in line with various other fields like Encoder, Coding (social sciences), Theoretical computer science and Discrete cosine transform. While IEEE Transactions on Circuits and Systems for Video Technology focused on Pattern recognition, it was also able to explore topics like Histogram, Feature (computer vision) and Robustness (computer science). Issues in Data compression were discussed, taking into consideration concepts from other disciplines like Quantization (signal processing), Transform coding, Real-time computing, Image compression and Signal compression.
The most cited papers are organized to reinforce research efforts on Artificial intelligence, Computer vision, Image processing, Data compression and Algorithm. The published papers investigate Artificial intelligence research which frequently intersects with Pattern recognition. The journal articles deal with Algorithm in conjunction with Encoder and similar fields in Decoding methods.
IEEE Transactions on Circuits and Systems for Video Technology facilitates discussions on Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Feature (computer vision). Discussions in IEEE Transactions on Circuits and Systems for Video Technology are anchored in the subject of Artificial intelligence and the similar topic of Machine learning. The studies on Pattern recognition discussed can also contribute to research in the domains of Pixel, Representation (mathematics), Robustness (computer science) and Benchmark (computing).
Studies on Computer vision discussed in it link to the field of Frame (networking). While work presented in it provided substantial information on Feature extraction, it also covered topics in Feature (machine learning), Artificial neural network, Feature learning, Task analysis and Visualization. The study of Algorithm and how it intertwines with concepts under Algorithmic efficiency, Point cloud and Encoding (memory) were explored in the presented Image (mathematics) research.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in IEEE Transactions on Circuits and Systems for Video Technology (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in IEEE Transactions on Circuits and Systems for Video Technology (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 13.45% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 31.25% were posted by at least one author from the top 10 institutions publishing in the journal. Another 17.40% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 21.28% of all publications and 30.07% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
If you're an aspiring researcher or student with an interest in the fields of Artificial Intelligence, Computer Vision, Algorithm Analysis, and Data Compression among others, it might be worth considering a career as a high school history teacher. Why, you might ask? A career in education not only offers a chance to influence and inspire the next generation of researchers but also provides ample time and opportunity for you to pursue your own research interests. Moreover, being in an educational environment could offer a refreshing lens to perceive and approach research topics.
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Once equipped with the right qualifications, you could leverage this opportunity not just to educate younger minds, but also to delve deeper into your research interests. Such an approach might even provide unique research questions and insights that could lead to contributions in prestigious journals like the IEEE Transactions on Circuits and Systems for Video Technology. So, if you've been contemplating your next career move and love both teaching and research, blend these passions and embark on an enriching journey of knowledge acquisition and dissemination.
This unique combination of education and research can turn out to be a fulfilling pathway, enabling you to stay connected to academia while shaping future generations. Endless possibilities could be just a career decision away.
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