论文标题
ELIC：有效学习的图像压缩，具有不均分组的空间通道上下文编码
ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding
论文作者
论文摘要
最近，学到的图像压缩技术已经达到了出色的性能，甚至超过了手动设计的有损图像编码器。他们承诺将被大规模采用。为了实用，对于压缩性能和跑步速度，对学习图像压缩的架构设计的彻底研究至关重要。在本文中，我们首先提出了由学到的图像压缩中能量压实的观察到的不平衡通道条件自适应编码。将提出的不平坦分组模型与现有上下文模型相结合，我们获得了空间通道上下文自适应模型，以提高编码性能而不会损害运行速度。然后，我们研究主要变换的结构，并提出了一个有效的模型ELIC，以实现最新的速度和压缩能力。凭借出色的性能，提出的模型还支持非常快速的预览解码和渐进的解码，这使得即将到来的基于学习的图像压缩的应用更加有前途。
Recently, learned image compression techniques have achieved remarkable performance, even surpassing the best manually designed lossy image coders. They are promising to be large-scale adopted. For the sake of practicality, a thorough investigation of the architecture design of learned image compression, regarding both compression performance and running speed, is essential. In this paper, we first propose uneven channel-conditional adaptive coding, motivated by the observation of energy compaction in learned image compression. Combining the proposed uneven grouping model with existing context models, we obtain a spatial-channel contextual adaptive model to improve the coding performance without damage to running speed. Then we study the structure of the main transform and propose an efficient model, ELIC, to achieve state-of-the-art speed and compression ability. With superior performance, the proposed model also supports extremely fast preview decoding and progressive decoding, which makes the coming application of learning-based image compression more promising.
