TY - JOUR
AU - Wu, Jiasheng
AB - Convolutional neural network architectures have been the primary choice for deep learning-based video compression algorithms in recent years, but common convolutional neural networks can only exploit local correlations, while compression is faced with a wide variety of data types, making the compression performance and generalisation ability of the model challenging. To address the above challenges, a transformer-based content adaptive video compression method (TAVC) is proposed in this paper, which can effectively improve the generalisation ability of the model while achieving better compression effects. Specifically, we exploit the non-local correlation between features and propose a transformer-based compression network for motion information coding and residual coding to further improve the performance of compression coding. In addition, we design a content-adaptive algorithm to choose the best encoder parameters for various videos. Experiments show that TAVC outperforms current mainstream deep learning-based video compression coding algorithms on the HEVC and UVG datasets, saving an average of 14.944% of the bit rate.
TI - A content-adaptive video compression method based on transformer
JF - International Journal of Computational Science and Engineering
DO - 10.1504/ijcse.2024.139770
DA - 2024-01-01
UR - https://www.deepdyve.com/lp/inderscience-publishers/a-content-adaptive-video-compression-method-based-on-transformer-9ZkqVyZhpq
SP - 495
EP - 503
VL - 27
IS - 4
DP - DeepDyve
ER -