Dataset Groups Activity Stream C3DVQA: FULL-REFERENCE VIDEO QUALITY ASSESSMENT WITH 3D CONVOLUTIONAL NEURAL NETWORK The proposed C3DVQA architecture is a full-reference video quality assessment method using 3D convolutional neural network. BibTex: @dataset{Munan_Xu_and_Junming_Chen_and_Haiqiang_Wang_and_Shan_Liu_and_Ge_Li_2024, abstract = {The proposed C3DVQA architecture is a full-reference video quality assessment method using 3D convolutional neural network.}, author = {Munan Xu and Junming Chen and Haiqiang Wang and Shan Liu and Ge Li}, doi = {10.57702/lie6ny35}, institution = {No Organization}, keyword = {'3D convolutional neural network', 'temporal masking effect', 'video quality assessment'}, month = {dec}, publisher = {TIB}, title = {C3DVQA: FULL-REFERENCE VIDEO QUALITY ASSESSMENT WITH 3D CONVOLUTIONAL NEURAL NETWORK}, url = {https://service.tib.eu/ldmservice/dataset/c3dvqa--full-reference-video-quality-assessment-with-3d-convolutional-neural-network}, year = {2024} }
