Ghost-NeRV: Efficient Neural Video Representation via Ghost Convolutions
Neural video representation (NeRV) has emerged as an efficient paradigm for video compression by encoding entire sequences into neural network parameters. Despite its strong reconstruction capability, NeRV suffers from high computational cost due to expensive convolutional operations in the decoder,...
Saved in:
| Main Author: | Shanableh, Tamer (author) |
|---|---|
| Format: | article |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/11073/33302 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Binary-NeRV: Hybrid-Precision Weights Binarization for Efficient Neural Video Representation
by: Shanableh, Tamer
Published: (2026) -
Error resiliency transcoding and decoding solutions using distributed video coding techniques
by: Shanableh, Tamer
Published: (2008) -
Predicting Split Decisions in MPEG-2 to HEVC Video Transcoding
by: Shanableh, Tamer
Published: (2020) -
Multilayer Transcoding with format portability for multicasting of single-layered video
by: Shanableh, Tamer
Published: (2005) -
MPEG-2 to HEVC Video Transcoding With Content-Based Modeling
by: Shanableh, Tamer
Published: (2013)