Quality-blind compressed color image enhancement with convolutional neural networks

Lossy compressed images and videos suffer from visible compression artifacts, especially when the bit-rate is low. To improve the quality of the compressed image while keeping the same bit-rate, decoder-side compression artifacts reduction (CAR) becomes important. Recently, convolutional neural networks are adopted for CAR tasks and achieve the state-of-the-art performance. However, most CAR algorithms only focus on the reconstruction of the luminance channel. Also, a separate model usually needs to be trained for each quality factor (QF), which makes these approaches not practical in existing codecs. In this paper, we analyze a quality-blind training strategy and compare it with training separate models for each QF. The testing results with three representative CAR algorithms show the superiority of the quality-blind training compared to separate training. The results for pseudo and real quality-blind CAR tests further prove the generalizability of the quality-blind training for practical CAR tasks.