  Learning-based Neural Video Codecs (NVCs) have emerged as a compelling
alternative to the standard video codecs, demonstrating promising performance,
and simple and easily maintainable pipelines. However, NVCs often fall short of
compression performance and occasionally exhibit poor generalization capability
due to inference-only compression scheme and their dependence on training data.
The instance-adaptive video compression techniques have recently been suggested
as a viable solution, fine-tuning the encoder or decoder networks for a
particular test instance video. However, fine-tuning all the model parameters
incurs high computational costs, increases the bitrates, and often leads to
unstable training. In this work, we propose a parameter-efficient
instance-adaptive video compression framework. Inspired by the remarkable
success of parameter-efficient fine-tuning on large-scale neural network
models, we propose to use a lightweight adapter module that can be easily
attached to the pretrained NVCs and fine-tuned for test video sequences. The
resulting algorithm significantly improves compression performance and reduces
the encoding time compared to the existing instant-adaptive video compression
algorithms. Furthermore, the suggested fine-tuning method enhances the
robustness of the training process, allowing for the proposed method to be
widely used in many practical settings. We conducted extensive experiments on
various standard benchmark datasets, including UVG, MCL-JVC, and HEVC
sequences, and the experimental results have shown a significant improvement in
rate-distortion (RD) curves (up to 5 dB PSNR improvements) and BD rates
compared to the baselines NVC. Our code is available on
https://github.com/ohsngjun/PEVC}{https://github.com/ohsngjun/PEVC.
