Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Perceptual Quality Assessment of Omnidirectional Images
Authors: Yuming Fang, Liping Huang, Jiebin Yan, Xuelin Liu, Yang Liu580-588
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on our database verify that the proposed model achieves the competing performance compared with the state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Yuming Fang1, Liping Huang1, Jiebin Yan1*, Xuelin Liu1, Yang Liu2 1 Jiangxi University of Finance and Economics, Nanchang, China 2 SANY Heavy Industry Co., Ltd., China |
| Pseudocode | No | No pseudocode or algorithm blocks labeled as such were found in the paper. The paper presents a framework diagram and mathematical equations but not structured code. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link, nor does it explicitly state that the code will be released or is available in supplementary materials. |
| Open Datasets | No | The paper describes the construction of 'our database' but does not provide any access information (link, DOI, citation) for this specific dataset to be publicly available. |
| Dataset Splits | Yes | We use 80% omnidirectional images of the proposed database for training and the rest 20% for testing. We take 10 times of random train-test splitting operation and report the median performance to reduce any bias. |
| Hardware Specification | Yes | The proposed model is implemented with Py Torch on an NVIDIA Ge Force GTX 1080 Ti machine. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number, nor does it list any other software dependencies with their versions. |
| Experiment Setup | Yes | We adopt Adam (Kingma and Ba 2017) optimizer with weight decay 5 10 4 and set mini-batch size to 4 for training 100 epochs, where the learning takes roughly a half-day. The initial learning rate is set to 10 4 and reduced by a decay factor 0.1 for 50 epochs. |