  Content-adaptive compression is crucial for enhancing the adaptability of the
pre-trained neural codec for various contents. Although these methods have been
very practical in neural image compression (NIC), their application in neural
video compression (NVC) is still limited due to two main aspects: 1), video
compression relies heavily on temporal redundancy, therefore updating just one
or a few frames can lead to significant errors accumulating over time; 2), NVC
frameworks are generally more complex, with many large components that are not
easy to update quickly during encoding. To address the previously mentioned
challenges, we have developed a content-adaptive NVC technique called
Group-aware Parameter-Efficient Updating (GPU). Initially, to minimize error
accumulation, we adopt a group-aware approach for updating encoder parameters.
This involves adopting a patch-based Group of Pictures (GoP) training strategy
to segment a video into patch-based GoPs, which will be updated to facilitate a
globally optimized domain-transferable solution. Subsequently, we introduce a
parameter-efficient delta-tuning strategy, which is achieved by integrating
several light-weight adapters into each coding component of the encoding
process by both serial and parallel configuration. Such architecture-agnostic
modules stimulate the components with large parameters, thereby reducing both
the update cost and the encoding time. We incorporate our GPU into the latest
NVC framework and conduct comprehensive experiments, whose results showcase
outstanding video compression efficiency across four video benchmarks and
adaptability of one medical image benchmark.
