FrameCorr: Adaptive, Autoencoder-based Neural Compression for Video Reconstruction in Resource and Timing Constrained Network Settings
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 23 references that are in the map. First posted on arXiv 4 Sep 2024.
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References · 5
- 2016End-to-end Optimized Image Compressioncited by 673
- 2017Lossy Image Compression with Compressive Autoencoderscited by 289
- 2015Variable Rate Image Compression with Recurrent Neural Networkscited by 168
- 2021Progressive Neural Image Compression with Nested Quantization and Latent Orderingcited by 24
- 2023Progressive Neural Compression for Adaptive Image Offloading under Timing Constraintscited by 3
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