论文标题
图像压缩具有学识渊博的基于提升的DWT和学习的基于树的熵模型
Image Compression With Learned Lifting-Based DWT and Learned Tree-Based Entropy Models
论文作者
论文摘要
本文探讨了基于传统和学到的离散小波变换（DWT）体系结构和用于编码DWT子带系数的熵模型的学术图像压缩。通过实现的非线性预测和更新过滤器，通过提升方案获得了学习的DWT。提出了几种学习的熵模型，以利用类似于传统的EZW，SPIHT或EBCOT算法的间和内部子带系数依赖性。实验结果表明，当提出的学习熵模型与传统小波过滤器（例如CDF 9/7滤波器）结合使用时，可以实现远远超过JPEG2000的压缩性能。当学习的熵模型与学习的DWT结合时，压缩性能会进一步增加。学到的DWT和所有熵模型中的计算（除一个模型）都可以简单地平行，并且系统在GPU上提供了实用的编码和解码时间。
This paper explores learned image compression based on traditional and learned discrete wavelet transform (DWT) architectures and learned entropy models for coding DWT subband coefficients. A learned DWT is obtained through the lifting scheme with learned nonlinear predict and update filters. Several learned entropy models are proposed to exploit inter and intra-DWT subband coefficient dependencies, akin to traditional EZW, SPIHT, or EBCOT algorithms. Experimental results show that when the proposed learned entropy models are combined with traditional wavelet filters, such as the CDF 9/7 filters, compression performance that far exceeds that of JPEG2000 can be achieved. When the learned entropy models are combined with the learned DWT, compression performance increases further. The computations in the learned DWT and all entropy models, except one, can be simply parallelized, and the systems provide practical encoding and decoding times on GPUs.
