Non-Semantics Suppressed Mask Learning for Unsupervised Video Semantic Compression

Most video compression methods aim to improve the decoded video visual quality, instead of particularly guaranteeing the semantic-completeness, which deteriorates downstream video analysis tasks, e.g., action recognition. In this paper, we focus on a novel unsupervised video semantic compression problem, where video semantics is compressed in a downstream task-agnostic manner. To tackle this problem, we first propose a Semantic-Mining-then-Compensation (SMC) framework to enhance the plain video codec with powerful semantic coding capability. Then, we optimize the framework with only unlabeled video data, by masking out a proportion of the compressed video and reconstructing the masked regions of the original video, which is inspired by recent masked image modeling (MIM) methods. Although the MIM scheme learns generalizable semantic features, its inner generative learning paradigm may also facilitate the coding framework memorizing non-semantic information with extra bit costs. To suppress this deficiency, we explicitly decrease the non-semantic information entropy of the decoded video features, by formulating it as a parametrized Gaussian Mixture Model conditioned on the mined video semantics. Comprehensive experimental results demonstrate the proposed approach shows remarkable superiority over previous traditional, learnable, and perceptual quality-oriented video codecs, on three video analysis tasks and seven datasets.