Learned Video Compression via Joint Spatial-Temporal Correlation Exploration

Understand

Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency.

• Efficient temporal information representation plays a key role in video coding.
• Thus, in this paper, we propose to exploit the temporal correlation using both first-order optical flow and second-order flow prediction.
• We suggest an one-stage learning approach to encapsulate flow as quantized features from consecutive frames which is then entropy coded with adaptive contexts conditioned on joint spatial-temporal priors to exploit second-order correlations.