论文标题
用于VVC Intra Intra编码的多尺度分组密集网络
Multi-scale Grouped Dense Network for VVC Intra Coding
论文作者
论文摘要
与任何其他常规图像编解码器保持相同的位时，多功能视频编码（H.266/VVC）标准可以达到更好的图像质量，例如BPG，JPEG等。但是，基于传统编码技术的基础，提高图像质量具有高压比的图像质量仍然具有吸引力和挑战。在本文中，我们设计了多尺度分组密集网络（MSGDN），以通过组合多尺度和分组的密集块来进一步减少压缩伪像，这些块被整合为VVC Intra Intra编码的后进程网络。此外，为了提高压缩图像的主观质量，我们还通过利用我们的MSGDN作为生成器提出了生成的对抗网络（MSGDN-GAN）。在验证集的广泛实验中，通过MSE损失训练的MSGDN平均在低级轨道中以0.15的比率为0.15的PSNR平均为32.622。此外，我们的MSGDN-GAN可以实现更好的主观性能。
Versatile Video Coding (H.266/VVC) standard achieves better image quality when keeping the same bits than any other conventional image codec, such as BPG, JPEG, and etc. However, it is still attractive and challenging to improve the image quality with high compression ratio on the basis of traditional coding techniques. In this paper, we design the multi-scale grouped dense network (MSGDN) to further reduce the compression artifacts by combining the multi-scale and grouped dense block, which are integrated as the post-process network of VVC intra coding. Besides, to improve the subjective quality of compressed image, we also present a generative adversarial network (MSGDN-GAN) by utilizing our MSGDN as generator. Across the extensive experiments on validation set, our MSGDN trained by MSE losses yields the PSNR of 32.622 on average with teams IMC at the bit-rate of 0.15 in Lowrate track. Moreover, our MSGDN-GAN could achieve the better subjective performance.
