Open Access
Subscription Access
Open Access
Subscription Access
An Improvised Video Enhancement Using Machine Learning
Subscribe/Renew Journal
The visual quality of underexposed videos can be increased with the help of a process known as video enhancement. Within the scope of this research, we present a novel approach to enhancing the exposure of movies that are underexposed. Because it has a low barrier to entry theoretically and can reliably give perceptually pleasant outcomes without adding artifacts, it is ideal for a wide variety of practical applications. This is because it is useful for a wide variety of practical applications. We demonstrate the usefulness of the method by displaying improved films of good quality that were made from a variety of different sorts of underexposed videos. A novel approach to the enhancement and editing of video is presented by our method. Rather than relying on a single heuristic transform function, it makes use of human visual perception to adaptively customize the overall visual appearance of the finished product. We believe that our work has the potential to make a big influence in the field of perception-aware video editing and the applications that are related to it, and that it is an important contribution to the community that works on improving videos. The challenge of video enhancement can be formulated as follows: given a video of low quality as the input, produce a video of high quality as the output, but only for specific uses. Whether it be in terms of the video objective clarity or its more subjective qualities, we are interested in learning how we might improve its overall quality.
Keywords
Visual Appearance, Video Enhancement, Machine Learning.
Subscription
Login to verify subscription
User
Font Size
Information
- M. Bhende and V. Saravanan, Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection, BioMed Research International, Vol. 2022, pp. 1-9, 2022.
- K.C. Chan and C.C. Loy, BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment, Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5972-5981, 2022.
- C. Nithya and V. Saravanan, A Study of Machine Learning Techniques in Data Mining, International Journal of Scientific Research, Vol. 1, 31-38, 2018.
- D. Wu and R. Wang, Edge-Cloud Collaboration Enabled Video Service Enhancement: A Hybrid Human-Artificial Intelligence Scheme, IEEE Transactions on Multimedia, Vol. 23, pp. 2208-2221, 2021.
- S. Tulyakov, J. Erbach and D. Scaramuzza, Time Lens: Event-Based Video Frame Interpolation, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16155-16164, 2021.
- K. Asha and M. Rizvana, Human Vision System Region of Interest Based Video Coding, Compusoft, Vol. 2, No. 5, pp. 127-138, 2013.
- I. Dave, M.N. Rizve and M. Shah, TCLR: Temporal Contrastive Learning for Video Representation, Computer Vision and Image Understanding, Vol. 219, pp. 103406-103418, 2022.
- P. Karthika and P. Vidhya Saraswathi, IoT using Machine Learning Security Enhancement in Video Steganography Allocation for Raspberry Pi, Journal of Ambient Intelligence and Humanized Computing, Vol. 12, No. 6, pp. 5835-5844, 2021.
- S. Niklaus, Revisiting Adaptive Convolutions for Video Frame Interpolation, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1099-1109, 2021.
- S. Bertoni and A. Facoetti, Action Video Games Enhance Attentional Control and Phonological Decoding in Children with Developmental Dyslexia, Brain Sciences, Vol. 11, No. 2, pp. 171-181, 2021.
- S. Yang, Y. Shan and W. Liu, Crossover Learning for Fast Online Video Instance Segmentation, Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8043-8052, 2011.
- S. Selvi and V. Saravanan,. Mapping and Classification of Soil Properties from Text Dataset using Recurrent Convolutional Neural Network, ICTACT Journal on Soft Computing, Vol. 11, No. 4, pp. 2438-2443, 2021.
Abstract Views: 88
PDF Views: 1