Fetching the paper…
Reading the bibliography…
Recently, User-Generated Content (UGC) videos have gained popularity in our daily lives.
Lubin, J.: A human vision system model for objective picture quality measurements. In: 1997 International Broadcasting Convention IBS 97, pp. 498–503 (1997)
1997
Earlier work this paper cites.
Hasler, D., Süsstrunk, S.: Measuring colourfulness in natural images. (2003)
2003
Earlier work this paper cites.
Wang, C., Ye, Z.: Brightness preserving histogram equalization with maximum entropy: a variational perspective. IEEE Transactions on Consumer Electronics 51
2005
Earlier work this paper cites.
Ooi, C.H., Mat Isa, N.A.: Adaptive contrast enhancement methods with brightness preserving. IEEE Transactions on Consumer Electronics 56
2010
Earlier work this paper cites.
Soundararajan, R., Bovik, A.C.: Video quality assessment by reduced reference spatio-temporal entropic differencing. IEEE Transactions on Circuits and Systems for Video Technology 23
2013
Earlier work this paper cites.
Saad, M.A., Bovik, A.C., Charrier, C.: Blind prediction of natural video quality. IEEE Transactions on Image Processing 23
2014
Earlier work this paper cites.
Saad, M.A., Bovik, A.C., Charrier, C.: Blind prediction of natural video quality. IEEE Transactions on Image Processing 23
2014
Earlier work this paper cites.
Long, J., Shelhamer, E., Darrell, T.: Fully Convolutional Networks for Semantic Segmentation (2015)
2015
Earlier work this paper cites.
Gupta, B., Tiwari, M.: Minimum mean brightness error contrast enhancement of color images using adaptive gamma correction with color preserving framework. Optik 127
2016
Earlier work this paper cites.
Thomee, B., Shamma, D.A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., Li, L.: Yfcc100m: The new data in multimedia research. Communications of the ACM 59
2016
Earlier work this paper cites.
ByteDance: CapCut (2017)
2017
Earlier work this paper cites.
Hosu, V., Hahn, F., Jenadeleh, M., Lin, H., Men, H., Szirányi, T., Li, S., Saupe, D.: The konstanz natural video database (konvid-1k). In: 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX), pp. 1–6 (2017)
2017
Earlier work this paper cites.
Lv, F., Lu, F., Wu, J., Lim, C.: Mbllen: Low-light image/video enhancement using cnns. In: BMVC, vol. 220, p. 4 (2018). Northumbria University
2018
Earlier work this paper cites.
Liu, X., Chen, L., Wang, W., Zhao, J.: Robust multi-frame super-resolution based on spatially weighted half-quadratic estimation and adaptive btv regularization. IEEE Transactions on Image Processing (2018)
2018
Earlier work this paper cites.
Cao, G., Huang, L., Tian, H., Huang, X., Wang, Y., Zhi, R.: Contrast enhancement of brightness-distorted images by improved adaptive gamma correction. Computers & Electrical Engineering 66
2018
Earlier work this paper cites.
Ghadiyaram, D., Pan, J., Bovik, A.C., Moorthy, A.K., Panda, P., Yang, K.-C.: In-capture mobile video distortions: A study of subjective behavior and objective algorithms. IEEE Transactions on Circuits and Systems for Video Technology 28
2018
Earlier work this paper cites.
Zhang, Q., Nie, Y., Zheng, W.-S.: Dual Illumination Estimation for Robust Exposure Correction (2019)
2019
Earlier work this paper cites.
Chen, C., Chen, Q., Do, M.N., Koltun, V.: Seeing motion in the dark. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
2019
Earlier work this paper cites.
Bampis, C.G., Li, Z., Bovik, A.C.: Spatiotemporal feature integration and model fusion for full reference video quality assessment. IEEE Transactions on Circuits and Systems for Video Technology 29
2019
Earlier work this paper cites.
Sinno, Z., Bovik, A.C.: Large-scale study of perceptual video quality. IEEE Transactions on Image Processing 28
2019
Earlier work this paper cites.
Li, Y., Meng, S., Zhang, X., Wang, S., Wang, Y., Ma, S.: UGC-VIDEO: perceptual quality assessment of user-generated videos (2019)
2019
Earlier work this paper cites.
Wang, Y., Inguva, S., Adsumilli, B.: Youtube ugc dataset for video compression research. In: 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), pp. 1–5 (2019)
2019
Earlier work this paper cites.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. (2019)
2019
Earlier work this paper cites.
Korhonen, J.: Two-level approach for no-reference consumer video quality assessment. IEEE Transactions on Image Processing 28
2019
Earlier work this paper cites.
Li, D., Jiang, T., Jiang, M.: Quality assessment of in-the-wild videos. In: Proceedings of the 27th ACM International Conference on Multimedia. MM ’19. ACM, ??? (2019)
2019
Earlier work this paper cites.
Feichtenhofer, C., Fan, H., Malik, J., He, K.: SlowFast Networks for Video Recognition (2019)
2019
Earlier work this paper cites.
Somal, S.: Image enhancement using local and global histogram equalization technique and their comparison. In: Luhach, A.K., Kosa, J.A., Poonia, R.C., Gao, X.-Z., Singh, D. (eds.) First International Conference on Sustainable Technologies for Computational Intelligence, pp. 739–753. Springer, Singapore (2020)
2020
Earlier work this paper cites.
Liu, X., Kong, L., Zhou, Y., Zhao, J., Chen, J.: End-to-end trainable video super-resolution based on a new mechanism for implicit motion estimation and compensation. In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2020)
2020
Earlier work this paper cites.
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., Amodei, D.: Language Models are Few-Shot Learners (2020)
2020
Cited alongside, same era.
Afifi, M., Derpanis, K.G., Ommer, B., Brown, M.S.: Learning multi-scale photo exposure correction. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9153–9163 (2021)
2021
Cited alongside, same era.
Nsampi, N.E., Hu, Z., Wang, Q.: Learning exposure correction via consistency modeling. In: British Machine Vision Conference (2021)
2021
Cited alongside, same era.
Zhang, F., Li, Y., You, S., Fu, Y.: Learning temporal consistency for low light video enhancement from single images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4967–4976 (2021)
Wang, H., Xu, K., Lau, R.W.H.: Local color distributions prior for image enhancement. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision – ECCV 2022, pp. 343–359. Springer, Cham (2022)
2022
Later among the works it cites.
Li, B., Zhang, W., Tian, M., Zhai, G., Wang, X.: Blindly assess quality of in-the-wild videos via quality-aware pre-training and motion perception. IEEE Transactions on Circuits and Systems for Video Technology 32
2022
Later among the works it cites.
2022
Later among the works it cites.
Wu, H., Zhang, E., Liao, L., Chen, C., Hou, J., Wang, A., Sun, W., Yan, Q., Lin, W.: Towards explainable video quality assessment: A database and a language-prompted approach. In: ACM MM (2023)
2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
Shi, Z., Liu, X., Li, C., Dai, L., Chen, J., Davidson, T.N., Zhao, J.: Learning for unconstrained space-time video super-resolution. IEEE Transactions on Broadcasting (2021)
2021
Cited alongside, same era.
Liu, X., Shi, K., Wang, Z., Chen, J.: Exploit camera raw data for video super-resolution via hidden markov model inference. IEEE Transactions on Image Processing (2021)
2021
Cited alongside, same era.
Shi, Z., Liu, X., Shi, K., Dai, L., Chen, J.: Video frame interpolation via generalized deformable convolution. IEEE Transactions on Multimedia (2021)
2021
Cited alongside, same era.
Wang, R., Xu, X., Fu, C.-W., Lu, J., Yu, B., Jia, J.: Seeing dynamic scene in the dark: A high-quality video dataset with mechatronic alignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9700–9709 (2021)
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning Transferable Visual Models From Natural Language Supervision (2021)
2021
Cited alongside, same era.
Gheini, M., Ren, X., May, J.: Cross-Attention is All You Need: Adapting Pretrained Transformers for Machine Translation (2021)
2021
Cited alongside, same era.
Yu, X., Birkbeck, N., Wang, Y., Bampis, C.G., Adsumilli, B., Bovik, A.C.: Predicting the quality of compressed videos with pre-existing distortions. IEEE Transactions on Image Processing 30
2021
Cited alongside, same era.
Ying, Z., Mandal, M., Ghadiyaram, D., Bovik, A.: Patch-vq: ‘patching up’ the video quality problem. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14014–14024 (2021)
2021
Cited alongside, same era.
Later among the works it cites.
Wu, H., Zhang, Z., Zhang, W., Chen, C., Liao, L., Li, C., Gao, Y., Wang, A., Zhang, E., Sun, W., Yan, Q., Min, X., Zhai, G., Lin, W.: Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels (2023)
2023
Later among the works it cites.
Dong, Y., Liu, X., Gao, Y., Zhou, X., Tan, T., Zhai, G.: Light-VQA: A Multi-Dimensional Quality Assessment Model for Low-Light Video Enhancement (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Wu, G., Liu, X., Luo, K., Liu, X., Zheng, Q., Liu, S., Jiang, X., Zhai, G., Wang, W.: Accflow: Backward accumulation for long-range optical flow. In: International Conference on Computer Vision (2023)
2023
Later among the works it cites.
Li, W., Wu, G., Wang, W., Ren, P., Liu, X.: Fastllve: Real-time low-light video enhancement with intensity-aware lookup table. In: The 31st ACM International Conference on Multimedia (2023)
2023
Later among the works it cites.
Zou, Z., Chen, K., Shi, Z., Guo, Y., Ye, J.: Object Detection in 20 Years: A Survey (2023)
2023
Later among the works it cites.
Zhang, Z., Li, C., Sun, W., Liu, X., Min, X., Zhai, G.: A perceptual quality assessment exploration for aigc images. In: IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Li, C., Zhang, Z., Wu, H., Sun, W., Min, X., Liu, X., Zhai, G., Lin, W.: Agiqa-3k: An open database for ai-generated image quality assessment. IEEE Transactions on Circuits and Systems for Video Technology (2023)
2023
Later among the works it cites.
Kou, T., Liu, X., Jia, J., Sun, W., Zhai, G., Liu, N.: Stablevqa: A deep no-reference quality assessment model for video stability. In: Proceedings of the 31st ACM International Conference on Multimedia (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Gao, Y., Cao, Y., Kou, T., Sun, W., Dong, Y., Liu, X., Min, X., Zhai, G.: VDPVE: VQA Dataset for Perceptual Video Enhancement (2023)
2023
Later among the works it cites.
Wang, J., Chan, K.C., Loy, C.C.: Exploring clip for assessing the look and feel of images. In: AAAI (2023)
2023
Later among the works it cites.
Zhang, W., Zhai, G., Wei, Y., Yang, X., Ma, K.: Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective (2023)
2023
Later among the works it cites.
Wu, H., Zhang, E., Liao, L., Chen, C., Hou, J.H., Wang, A., Sun, W.S., Yan, Q., Lin, W.: Exploring video quality assessment on user generated contents from aesthetic and technical perspectives. In: International Conference on Computer Vision (ICCV) (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual Instruction Tuning. NeurIPS (2023)
2023
Later among the works it cites.
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., Lample, G.: LLaMA: Open and Efficient Foundation Language Models (2023)
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Zhang, Z., Zhou, Y., Li, C., Fu, K., Sun, W., Liu, X., Min, X., Zhai, G.: A reduced-reference quality assessment metric for textured mesh digital humans. In: International Conference on Acoustics, Speech, and Signal Processing (2024)
2024
Closest in time.
Liu, H., Li, C., Li, Y., Li, B., Zhang, Y., Shen, S., Lee, Y.J.: LLaVA-NeXT: Improved reasoning, OCR, and world knowledge (2024)
2024
Closest in time.