David L. Neuhoff

5.0k total citations · 1 hit paper
156 papers, 3.3k citations indexed

About

David L. Neuhoff is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, David L. Neuhoff has authored 156 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 108 papers in Computer Vision and Pattern Recognition, 41 papers in Artificial Intelligence and 34 papers in Electrical and Electronic Engineering. Recurrent topics in David L. Neuhoff's work include Advanced Data Compression Techniques (81 papers), Algorithms and Data Compression (39 papers) and Image and Signal Denoising Methods (24 papers). David L. Neuhoff is often cited by papers focused on Advanced Data Compression Techniques (81 papers), Algorithms and Data Compression (39 papers) and Image and Signal Denoising Methods (24 papers). David L. Neuhoff collaborates with scholars based in United States, South Korea and Netherlands. David L. Neuhoff's co-authors include Robert M. Gray, Thrasyvoulos N. Pappas, Paul C. Shields, Marco Dima, Dennis Hui, Robin C. Gilbert, L. Davisson, J. Ribas-Corbera, Daniel De Marco and Stephen McLaughlin and has published in prestigious journals such as Proceedings of the IEEE, IEEE Transactions on Information Theory and IEEE Transactions on Image Processing.

In The Last Decade

David L. Neuhoff

139 papers receiving 3.1k citations

Hit Papers

Quantization 1998 2026 2007 2016 1998 250 500 750 1000
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Quantization
    1998 1.1k citations Robert M. Gray, David L. Neuhoff IEEE Transactions on Information Theory profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David L. Neuhoff United States 28 1.9k 816 798 685 610 156 3.3k
Arun N. Netravali United States 27 2.8k 1.5× 511 0.6× 277 0.3× 510 0.7× 947 1.6× 130 3.9k
Michael W. Marcellin United States 31 3.4k 1.8× 705 0.9× 678 0.8× 674 1.0× 1.5k 2.5× 199 4.9k
John W. Woods United States 28 2.6k 1.4× 505 0.6× 423 0.5× 464 0.7× 1.3k 2.1× 134 4.0k
Jonathan S. Yedidia United States 22 922 0.5× 1.0k 1.3× 1.4k 1.7× 1.4k 2.0× 401 0.7× 53 3.8k
Thrasyvoulos N. Pappas United States 28 2.0k 1.1× 583 0.7× 279 0.3× 420 0.6× 547 0.9× 154 3.2k
Naga K. Govindaraju United States 33 1.8k 1.0× 303 0.4× 982 1.2× 2.4k 3.5× 868 1.4× 80 5.5k
Andrew Lumsdaine United States 35 1.6k 0.9× 530 0.6× 1.1k 1.4× 2.2k 3.3× 173 0.3× 241 5.3k
N. Jayant United States 26 2.9k 1.5× 759 0.9× 681 0.9× 569 0.8× 2.0k 3.3× 135 4.0k
P.-A. Absil Belgium 19 981 0.5× 626 0.8× 687 0.9× 243 0.4× 395 0.6× 38 4.0k
J.W. Modestino United States 28 1.4k 0.8× 869 1.1× 531 0.7× 771 1.1× 738 1.2× 125 3.1k

Countries citing papers authored by David L. Neuhoff

Since Specialization
Citations

This map shows the geographic impact of David L. Neuhoff'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 David L. Neuhoff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David L. Neuhoff more than expected).

Fields of papers citing papers by David L. Neuhoff

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David L. Neuhoff. 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 David L. Neuhoff. The network helps show where David L. Neuhoff may publish in the future.

Co-authorship network of co-authors of David L. Neuhoff

This figure shows the co-authorship network connecting the top 25 collaborators of David L. Neuhoff. A scholar is included among the top collaborators of David L. Neuhoff based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David L. Neuhoff. David L. Neuhoff is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Neuhoff, David L., et al.. (2017). On the Convexity of the MSE Distortion of Symmetric Uniform Scalar Quantization. IEEE Transactions on Information Theory. 64(4). 2626–2638. 14 indexed citations
2.
Pappas, Thrasyvoulos N., et al.. (2013). Structural Texture Similarity Metrics for Image Analysis and Retrieval. IEEE Transactions on Image Processing. 22(7). 2545–2558. 82 indexed citations
3.
Neuhoff, David L. & S. Sandeep Pradhan. (2011). Information rates of densely sampled Gaussian data. 2776–2780. 1 indexed citations
4.
Neuhoff, David L., et al.. (2009). Arithmetic encoding of Markov random fields. 48. 532–536. 7 indexed citations
5.
Duarte‐Melo, Enrique J., et al.. (2006). The Effect of Node Density and Propagation Model on Throughput Scaling of Wireless Networks. 1693–1697. 12 indexed citations
6.
Dima, Marco, Enrique J. Duarte‐Melo, Mingyan Liu, & David L. Neuhoff. (2003). On the Many-to-One Transport Capacity of a Dense Wireless Sensor Network and the Compressibility of Its Data.
7.
Hui, Dennis & David L. Neuhoff. (2002). Asymptotic analysis of optimum uniform scalar quantizers for generalized Gaussian distributions. 461–461. 1 indexed citations
8.
9.
Gray, Robert M. & David L. Neuhoff. (2000). Quantization (invited paper). IEEE Press eBooks. 281–339. 2 indexed citations
10.
Ribas-Corbera, J. & David L. Neuhoff. (1996). Reducing rate/complexity in video coding by motion estimation with block adaptive accuracy.. Proc SPIE. 2727. 615–624. 1 indexed citations
11.
Ribas-Corbera, J. & David L. Neuhoff. (1996). Optimizing the motion vector accuracies in block-based video coding.. Deep Blue (University of Michigan). 2 indexed citations
12.
Neuhoff, David L., et al.. (1996). Asymptotic distribution of the errors in scalar and vector quantizers. IEEE Transactions on Information Theory. 42(2). 446–460. 45 indexed citations
13.
Neuhoff, David L.. (1996). On the asymptotic distribution of the errors in vector quantization. IEEE Transactions on Information Theory. 42(2). 461–468. 21 indexed citations
14.
Pappas, Thrasyvoulos N. & David L. Neuhoff. (1995). Printer models and error diffusion. IEEE Transactions on Image Processing. 4(1). 66–80. 56 indexed citations
15.
Neuhoff, David L. & Thrasyvoulos N. Pappas. (1994). Perceptual coding of images for halftone display. IEEE Transactions on Image Processing. 3(1). 1–13. 7 indexed citations
16.
Pappas, Thrasyvoulos N. & David L. Neuhoff. (1991). Model-based halftoning. Proceedings of SPIE - The International Society for Optical Engineering. 1453. 244–255. 3 indexed citations
17.
Neuhoff, David L., et al.. (1991). On the performance of tree-structured vector quantization. it 28. 2277–2280 vol.4. 10 indexed citations
18.
Neuhoff, David L., et al.. (1987). Robust source coding of weakly compact classes. IEEE Transactions on Information Theory. 33(4). 522–530. 3 indexed citations
19.
Neuhoff, David L. & Robin C. Gilbert. (1982). Causal source codes. IEEE Transactions on Information Theory. 28(5). 701–713. 79 indexed citations
20.
Neuhoff, David L., et al.. (1980). Average error probability for direct sequence spread spectrum multiple access communication systems. 359–368.

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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