ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Meta-Heuristic Optimisation for Optimal Video Quality Enhancement


Affiliations
1 Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, India
     

   Subscribe/Renew Journal


We have seen a rapid development in technology over the past few years, from basic mobile phones to highly advanced surveillance monitoring systems that can record and analyze video clips. The process of video capture inevitably leads to a decline in overall video recording quality. Inadequate lighting is due to either an open aperture or a slow shutter speed. Images taken in such conditions typically have poor contrast and noisy backgrounds. If the contrast in your video is off, it could be because of a malfunctioning imaging device or an untrained operator. These two outcomes are equally plausible. When recording videos, this causes a loss of the dynamic range that could have been captured. Because of this, the video may appear distorted or washed out, and some of the finer details may be lost. In order to lessen the impact of these problems on the viewer experience, contrast enhancement techniques can be used to boost the visual quality. Work presented here will take a two-pronged approach to addressing the aforementioned problems. Videos can be compressed and their contrast can be increased, both of which are useful techniques that complement one another. Video quality can be improved with the help of a method called ant colony optimisation ACO based image quality enhancement. The frames of a video can be analyzed in greater detail using this hybrid method than with the conventional method. The noise is further reduced when the non-divisible median filter is used. To do this, the study develops an optimisation to attain increased rate of peak signal to noise rate than the other existing methods. After examining available options, the researchers settled on the DLACDHE procedure as the best option. Based on the results, it is reasonable to infer that the proposed strategy offers better contrast enhancement than the conventional methods.

Keywords

Classification, Ant Colony Optimisation, Improved Video Quality Enhancement
Subscription Login to verify subscription
User
Notifications
Font Size

  • Ivan Laptev and Patrick Perez, “Retrieving Actions in Movies”, Proceedings of IEEE International Conference on Computer Vision, pp. 1-8, 2007.

  • Mun Wai Lee, Asaad Hakeem, Niels Haering and Song Chun Zhu, “Save: A Framework for Semantic Annotation of Visual Events”, Proceedings of IEEE Computer Vision and Pattern Recognition Workshops, pp. 1-8, 2008.

  • Mrunmayee Patil and Ramesh Kagalkar, “An Automatic Approach for Translating Simple Images into Text Descriptions and Speech for Visually Impaired People”, International Journal of Computer Applications, Vol. 118, No. 3, pp. 14-19, 2015.

  • Duo Ding et al., “Beyond Audio and Video Retrieval: Towards Multimedia Summarization”, Proceedings of ACM International Conference on Multimedia Retrieval, pp. 1-8, 2012.

  • Mrunmayee Patil and Ramesh Kagalkar, “A Review On Conversion of Image To Text as Well as Speech using Edge Detection and Image Segmentation”, International Journal of Science and Research, Vol. 3, No. 11, pp. 2164-2167, 2014.

  • Kishore K. Reddy and Mubarak Shah, “Recognizing 50 Human Action Categories of Web Videos”, Machine Vision and Applications, Vol. 24, No. 5, pp. 971-981, 2013

  • Anant M. Bagade and Sanjay N.Talbar, “A High Quality Steganography Musing Morphing”, Journal of International Process System, Vol. 10, No. 2, pp. 256-270,2014.

  • Geeta Kasana, Kulbir Singh and Stavanger Singh Bhatia, “Data Hiding Algorithm for Images using Discrete Wavelet Transform and Arnold Transform”, Journal of Information Processing System, Vol. 13, No. 5, pp. 1331-1344, 2015.

  • F. Arab, M. Shahidan and S. Abdullah, “A Robust Video Watermarking Technique for the Tamper Detection of Surveillance System”, Multimedia Tools and Application, Vol. 75, No. 18, pp. 10855-10865, 2016.

  • K.T. Atanassov, “Intuitionistic fuzzy sets”, Fuzzy Sets and Systems, Vol. 20, No. 1, pp. 87-96, 1986.

  • L.A. Zadeh, “Fuzzy Sets”, Information and Control, Vol. 8, No. 3, pp. 353-383, 1965.

  • G. Priest, “Paraconsistent Logic”, Kuluwer Academic Publishers, 2002.

  • W. Bruno, “Dialethesim, Logical Consequence and Hierarchy”, Analysis, Vol. 64, No. 4, pp. 318-326, 2004.

  • Ahmed M. Ayoup, Amr H Hussein and Mahmoud Ali, “Efficient Selective Image Encryption”, Multimedia Tools and Applications, Vol. 75, No. 24, pp. 17171-17186, 2016.

  • Tao Xiang, Jia Hu and Jianglin Sun, “Outsourcing Chaotic Selective Image Encryption to the Cloud with Steganography”, Digital Signal Processing, Vol. 43, pp. 28- 37, 2015.

  • Z. Pan and S. Kowng, “Efficient In-Loop Filtering based on Enhanced Deep Convolutional Neural Networks for HEVC”, IEEE Transactions on Image Processing, Vol. 29, pp. 5352-5366, 2020.

  • Y. Dai and F. Wu, “A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding”, Proceedings of International Conference on Multimedia Modeling, pp. 28-39, 2017.

  • D. Ma, F. Zhang and D.R. Bull, “MFRNet: A New CNN Architecture for Post-Processing and In-Loop Filtering”, IEEE Journal of Selected Topics in Signal Processing, Vol. 15, No. 2, pp. 378-387, 2020.

  • S.H. Park and J.W. Kang, “Fast Multi-Type Tree Partitioning for Versatile Video Coding using a Lightweight Neural Network”, IEEE Transactions on Multimedia, Vol. 23, pp. 4388-4399, 2020.


Abstract Views: 134

PDF Views: 4




  • Meta-Heuristic Optimisation for Optimal Video Quality Enhancement

Abstract Views: 134  |  PDF Views: 4

Authors

J. Jasmine
Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, India

Abstract


We have seen a rapid development in technology over the past few years, from basic mobile phones to highly advanced surveillance monitoring systems that can record and analyze video clips. The process of video capture inevitably leads to a decline in overall video recording quality. Inadequate lighting is due to either an open aperture or a slow shutter speed. Images taken in such conditions typically have poor contrast and noisy backgrounds. If the contrast in your video is off, it could be because of a malfunctioning imaging device or an untrained operator. These two outcomes are equally plausible. When recording videos, this causes a loss of the dynamic range that could have been captured. Because of this, the video may appear distorted or washed out, and some of the finer details may be lost. In order to lessen the impact of these problems on the viewer experience, contrast enhancement techniques can be used to boost the visual quality. Work presented here will take a two-pronged approach to addressing the aforementioned problems. Videos can be compressed and their contrast can be increased, both of which are useful techniques that complement one another. Video quality can be improved with the help of a method called ant colony optimisation ACO based image quality enhancement. The frames of a video can be analyzed in greater detail using this hybrid method than with the conventional method. The noise is further reduced when the non-divisible median filter is used. To do this, the study develops an optimisation to attain increased rate of peak signal to noise rate than the other existing methods. After examining available options, the researchers settled on the DLACDHE procedure as the best option. Based on the results, it is reasonable to infer that the proposed strategy offers better contrast enhancement than the conventional methods.

Keywords


Classification, Ant Colony Optimisation, Improved Video Quality Enhancement

References