Evaluation of Rounding Functions in Nearest-Neighbor Interpolation
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Abstract (arXiv)
A novel evaluation study of the most appropriate round function for nearest-neighbor (NN) image interpolation is presented. Evaluated rounding functions are selected among the five rounding rules defined by the Institute of Electrical and Electronics Engineers (IEEE) 754-2008 standard. Both full- and no-reference image quality assessment (IQA) metrics are used to study and evaluate the influence of rounding functions on NN interpolation image quality. The concept of achieved occurrences over targeted occurrences is used to determine the percentage of achieved occurrences based on the number of test images used. Inferential statistical analysis is applied to deduce from a small number of images and draw a conclusion of the behavior of each rounding function on a bigger number of images. Under the normal distribution and at the level of confidence equals to 95%, the maximum and minimum achievable occurrences by each evaluated rounding function are both provided based on the inferential analysis-based experiments.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Mar 2020.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
7 papers.
- 2021Effects of Image Size on Deep Learningcited by 7
- 2023Soft labelling for semantic segmentation: Bringing coherence to label down-samplingcited by 5
- 2021Stochastic Rounding for Image Interpolation and Scan Conversioncited by 2
- 2025Beyond Nearest Neighbor Interpolation in Data Augmentationcited by 1
- 2021Challenges of 3D Surface Reconstruction in Capsule Endoscopycited by 0
- 2024How Homogenizing the Channel-wise Magnitude Can Enhance EEG Classification Model?cited by 0
- 2026Toward Optimal Adenovirus Detection Using YOLO26cited by 0
- 2026Toward Optimal Adenovirus Detection Using YOLO2620 Jul 2026
- 2025Beyond Nearest Neighbor Interpolation in Data Augmentation2 Apr 2025
- 2024How Homogenizing the Channel-wise Magnitude Can Enhance EEG Classification Model?19 Jul 2024
- 2023Soft labelling for semantic segmentation: Bringing coherence to label down-sampling27 Feb 2023
- 2021Stochastic Rounding for Image Interpolation and Scan Conversion25 Oct 2021
- 2021Challenges of 3D Surface Reconstruction in Capsule Endoscopy18 Mar 2021
- 2021Effects of Image Size on Deep Learning27 Jan 2021