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Bibliographic Details
Main Authors: Li, John, Ahmed, Shehab Sarar, Nair, Deepak
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2409.02453
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author Li, John
Ahmed, Shehab Sarar
Nair, Deepak
author_facet Li, John
Ahmed, Shehab Sarar
Nair, Deepak
contents Despite the growing adoption of video processing via Internet of Things (IoT) devices due to their cost-effectiveness, transmitting captured data to nearby servers poses challenges due to varying timing constraints and scarcity of network bandwidth. Existing video compression methods face difficulties in recovering compressed data when incomplete data is provided. Here, we introduce FrameCorr, a deep-learning based solution that utilizes previously received data to predict the missing segments of a frame, enabling the reconstruction of a frame from partially received data.
format Preprint
id arxiv_https___arxiv_org_abs_2409_02453
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle FrameCorr: Adaptive, Autoencoder-based Neural Compression for Video Reconstruction in Resource and Timing Constrained Network Settings
Li, John
Ahmed, Shehab Sarar
Nair, Deepak
Image and Video Processing
Computer Vision and Pattern Recognition
Emerging Technologies
Multimedia
Despite the growing adoption of video processing via Internet of Things (IoT) devices due to their cost-effectiveness, transmitting captured data to nearby servers poses challenges due to varying timing constraints and scarcity of network bandwidth. Existing video compression methods face difficulties in recovering compressed data when incomplete data is provided. Here, we introduce FrameCorr, a deep-learning based solution that utilizes previously received data to predict the missing segments of a frame, enabling the reconstruction of a frame from partially received data.
title FrameCorr: Adaptive, Autoencoder-based Neural Compression for Video Reconstruction in Resource and Timing Constrained Network Settings
topic Image and Video Processing
Computer Vision and Pattern Recognition
Emerging Technologies
Multimedia
url https://arxiv.org/abs/2409.02453