High-Fidelity Video Compression based on Invertible Neural Transform and Implicit Conditioning
Learning-based video compression has recently achieved competitive rate-distortion performance compared to conventional video codecs. However, most existing methods rely on non-invertible analysis-synthesis transforms, with reconstruction quality subject to both quantization and transform approximation errors. This limitation becomes particularly restrictive at higher quality points, where quantization errors are small and transform-induced distortion dominates. To address this, we propose InnVC, an Invertible neural network based Video Codec for wide-range and high-fidelity compression. The core idea is to preserve an invertible main transform path prior to quantization, while injecting ...
| Authors | Siyue Teng, Ho Man Kwan, Yuxuan Jiang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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