Perceptual Quality Assessment of Omnidirectional Images
Authors: Yuming Fang, Liping Huang, Jiebin Yan, Xuelin Liu, Yang Liu580-588
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. |