Abstract：In recent years， high throughput image transmission has become widely used due to its ability to obtain graphic details. Under the circumstances of limited bandwidth and low temporal correlation between images， image coding strategies should satisfy with the real-time requirement and bandwidth available. In lossy compression algorithms， the quantization parameter (QP) affects greatly both output bitrate and image quality. Different from QP optimization strategies based on numerical image features such as the sum of absolute transformed difference (SATD)， phase congruency (PC)， structural similarity (SSIM)， a CNN-based end-to-end rate control solution was proposed， which predict the optimal QP directly from images. Trained on Inria Aerial Image Labeling Dataset， the refined rate control model is robust under real-time scenes. Experimental results show that the proposed end-to-end rate control method can achieve the target bitrates by 10.31% bit rate accuracy (BRA) more accurately than the original rate control algorithms based on numerical image features. The proposed method also achieves 8.57% BRA gain compared to multilayer perceptron (MLP) method.
2022, Vol. 56 
