论文标题
使用有损压缩学习更好的无损压缩
Learning Better Lossless Compression Using Lossy Compression
论文作者
论文摘要
我们利用强大的有损图像压缩算法BPG来构建无损图像压缩系统。具体而言，首先将原始图像分解为用BPG和相应残留物压缩后获得的有损重建。然后，我们将残留物的分布与基于卷积神经网络的概率模型进行建模，该模型以BPG重建为条件，并将其与熵编码结合到无损编码残差。最后，使用BPG和学习的残留编码器产生的BOTSTREAM的串联来存储图像。所得的压缩系统在学习的无损完整图像压缩中实现了最先进的性能，优于以前的学习方法以及PNG，WebP和JPEG2000。
We leverage the powerful lossy image compression algorithm BPG to build a lossless image compression system. Specifically, the original image is first decomposed into the lossy reconstruction obtained after compressing it with BPG and the corresponding residual. We then model the distribution of the residual with a convolutional neural network-based probabilistic model that is conditioned on the BPG reconstruction, and combine it with entropy coding to losslessly encode the residual. Finally, the image is stored using the concatenation of the bitstreams produced by BPG and the learned residual coder. The resulting compression system achieves state-of-the-art performance in learned lossless full-resolution image compression, outperforming previous learned approaches as well as PNG, WebP, and JPEG2000.
