Prior research on deep video compression (DVC) for machine tasks typically necessitates
training a unique codec for each specific task mandating a dedicated decoder per task. In …

Video compression has always been a popular research area, where many traditional and
deep video compression methods have been proposed. These methods typically rely on …

Recently, learning based video compression methods attract increasing attention. However,
the previous works suffer from error propagation due to the accumulation of reconstructed …

Conventional video compression approaches use the predictive coding architecture and
encode the corresponding motion information and residual information. In this paper, taking …

We propose an end-to-end learned video compression scheme for low-latency scenarios.
Previous methods are limited in using the previous one frame as reference. Our method …

Most of the existing deep learning-based video compression frameworks rely on motion
estimation and compensation. However, the artifacts of the warped frames after motion …

Deep video compression is attracting increasing attention from both deep learning and
video processing community. Recent learning-based approaches follow the hybrid coding …

Most of the existing neural video compression methods adopt the predictive coding
framework, which first generates the predicted frame and then encodes its residue with the …

One of the core components of conventional (ie, non-learned) video codecs consists of
predicting a frame from a previously-decoded frame, by leveraging temporal correlations. In …

In this work, we propose the complexity-guided slimmable decoder (cgSlimDecoder) in
combination with skip-adaptive entropy coding (SaEC) for efficient deep video compression …