An Efficient Low-Loss Data Transmission Model for Noisy Networks
Keywords:
Noisy Networks, Intelligent Redundancy Injection, Whale Optimization Algorithm, Overlapping Windowed Samples, Contour let transformAbstract
Now the Internet become fully-grown, video streaming has mature in popularity and at present consumes more bandwidth than other applications. Due to the growing demand for high quality and dependable image and video data transmission, the need for efficient and low-loss data transmission models in noisy networks has become increasingly crucial. Existing models, however, have limitations including high loss and poor performance in noisy network environments. To overcome these limitations, we propose a novel data transmission model that employs intelligent redundancy injection on windowed samples that overlap and use contour let transforms for encoding and decoding the data samples. Our proposed model employs an intelligent redundancy embedding process for transmitted data, thereby improving the transmission's reliability and quality levels. The model's Peak Signal-to-Noise Ratio (PSNR) exceeds 40 dB, indicating a high level of data integrity and quality. Real-time use cases for this model include video conferencing, live streaming, and remote sensing applications where the transmission of high-quality image and video data must be both reliable and efficient. The ability of the proposed model to operate effectively in noisy network environments renders it a valuable resource for numerous applications in a variety of fields. In conclusion, the proposed model represents a substantial improvement over existing data transmission models, as it achieves high level of reliability, efficiency, and quality in noisy network environments.
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