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• W4281729474 abstract "Mobile multimedia communication requires considerable resources such as bandwidth and efficiency to support Quality-of-Service (QoS) and user Quality-of-Experience (QoE). To increase the available bandwidth, 5G network designers have incorporated Cognitive Radio (CR), which can adjust communication parameters according to the needs of an application. The transmission errors occur in wireless networks, which, without remedial action, will result in degraded video quality. Secure transmission is also a challenge for such channels. Therefore, this paper's innovative scheme VQProtect focuses on the visual quality protection of compressed videos by detecting and correcting channel errors while at the same time maintaining video end-to-end confidentiality so that the content remains unwatchable. For the purpose, a two-round secure process is implemented on selected syntax elements of the compressed H.264/AVC bitstreams. To uphold the visual quality of data affected by channel errors, a computationally efficient Forward Error Correction (FEC) method using Random Linear Block coding (with complexity of O(k(n-1)) is implemented to correct the erroneous data bits, effectively eliminating the need for retransmission. Errors affecting an average of 7-10% of the video data bits were simulated with the Gilbert-Elliot model when experimental results demonstrated that 90% of the resulting channel errors were observed to be recoverable by correctly inferring the values of erroneous bits. The proposed solution's effectiveness over selectively encrypted and error-prone video has been validated through a range of Video Quality Assessment (VQA) metrics." @default.
• W4281729474 created "2022-06-13" @default.
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• W4281729474 date "2022-05-26" @default.
• W4281729474 modified "2023-09-30" @default.
• W4281729474 title "VQProtect: Lightweight Visual Quality Protection for Error-Prone Selectively Encrypted Video Streaming" @default.
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• W4281729474 doi "https://doi.org/10.3390/e24060755" @default.
• W4281729474 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35741477" @default.
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