Learned Video Compression via Heterogeneous Deformable Compensation Network
Learned video compression has recently emerged as an essential research topic in developing advanced video compression technologies, where motion compensation is considered one of the most challenging issues. In this paper, we propose a learned video compression framework via heterogeneous deformable compensation strategy (HDCVC) to tackle the problems of unstable compression performance caused by single-size deformable kernels in downsampled feature domain. More specifically, instead of utilizing optical flow warping or single-size-kernel deformable alignment, the proposed algorithm extracts features from the two adjacent frames to estimate content-adaptive heterogeneous deformable (HetDeform) kernel offsets. Then we transform the reference features with the HetDeform convolution to accomplish motion compensation. Moreover, we design a Spatial-Neighborhood-Conditioned Divisive Normalization (SNCDN) to achieve more effective data Gaussianization combined with the Generalized Divisive Normalization. Furthermore, we propose a multi-frame enhanced reconstruction module for exploiting context and temporal information for final quality enhancement. Experimental results indicate that HDCVC achieves superior performance than the recent state-of-the-art learned video compression approaches.
Code (0)
등록된 구현이 없습니다.
Tasks
Motion CompensationOptical Flow EstimationVideo CompressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Multi-Scale Deformable Alignment and Content-Adaptive Inference for Flexible-Rate Bi-Directional Video Compression
The lack of ability to adapt the motion compensation model to video content is an important limitation of current end-to-end learned video compression models. This paper advances the state-of-the-art by proposing an adap…
Motion CompensationVideo CompressionLVC-LGMC: Joint Local and Global Motion Compensation for Learned Video Compression
Existing learned video compression models employ flow net or deformable convolutional networks (DCN) to estimate motion information. However, the limited receptive fields of flow net and DCN inherently direct their atten…
Motion CompensationVideo CompressionFVC: A New Framework towards Deep Video Compression in Feature Space
Learning based video compression attracts increasing attention in the past few years. The previous hybrid coding approaches rely on pixel space operations to reduce spatial and temporal redundancy, which may suffer from …
Motion CompensationMotion EstimationVideo CompressionHybrid Local-Global Context Learning for Neural Video Compression
In neural video codecs, current state-of-the-art methods typically adopt multi-scale motion compensation to handle diverse motions. These methods estimate and compress either optical flow or deformable offsets to reduce …
Motion CompensationMotion EstimationOptical Flow EstimationVideo CompressionExploring Long- and Short-Range Temporal Information for Learned Video Compression
Learned video compression methods have gained a variety of interest in the video coding community since they have matched or even exceeded the rate-distortion (RD) performance of traditional video codecs. However, many c…
Motion CompensationOptical Flow EstimationVideo Compression