MCL-JCV: A JND-based H.264/AVC video quality assessment dataset
By Haiqiang Wang, Weihao Gan, Sudeng Hu, J. Lin, Lina Jin, L. Song, Ping Wang, I. Katsavounidis, Anne Aaron, C.-C. Jay Kuo. International Conference on Information Photonics, 2016
398 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/8000335f46fa2eebc7f90fda7e44266b9d1ee992
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In Computer Vision and Pattern Recognition
- Luc Van Gool
- Ming-Hsuan Yang
- Dacheng Tao
- Trevor Darrell
- Yi Yang
- Xiaogang Wang
- Andrew Zisserman
- Jitendra Malik
Citations in the corpus
- Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation (2023, methodology): “The study is conducted on the 30 videos of the MCL-JCV dataset, resized to a resolution of 640 ˆ 360.”
- Scale-Space Flow for End-to-End Optimized Video Compression (2020, methodology): “Rate savings for each video in the MCL-JCV dataset [35].”
- UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content (2020, methodology): “Other similar databases targeting simulated compression and transmission distortions have been released subsequently, including EPFL-PoliMI [2], VQEG-HDTV [3], IVP [4], TUM 1080p50 [5], CSIQ [6], MCL-V [7], and MCL-JCV [8].”
- Neural Video Compression with Diverse Contexts (2023, methodology): “For testing YUV420 videos, HEVC B∼E [6], UVG [39], and MCL-JCV [56] are used.”
- NeRV: Neural Representations for Videos (2021, methodology): “2 Results on MCL-JCL dataset We provide the experiment results for video compression task on MCL-JCL [1]dataset in Figure 1a and Figure 1b.”
- Deep Contextual Video Compression (2021, methodology): “The testing data includes MCL-JCV dataset [29] (copyright can be found from this link 2 ), UVG dataset[35] (BY-NC license 3 ), and HEVC standard test videos (more details can be found in [34]).”
- FVC: A New Framework towards Deep Video Compression in Feature Space (2021, methodology): “In this section, we provide the experimental results to demonstrate the effectiveness of our proposed method FVC on four benchmark datasets HEVC [27], UVG [24], VTL [1] and MCLJCV [34].”