Recent deep learning models outperform standard lossy image compression codecs. However, applying these models on a patch-by-patch basis requires that each image patch be encoded and decoded independently. The influence from adjacent patches is therefore lost, leading to block artefacts at low bitrates. We propose the Binary Inpainting Network (BINet), an autoencoder framework which incorporates binary inpainting to reinstate interdependencies between adjacent patches, for improved patch-based compression of still images. When decoding a patch, BINet additionally uses the binarised encodings from surrounding patches to guide its reconstruction. In contrast to sequential inpainting methods where patches are decoded based on previous reconstructions, BINet operates directly on the binary codes of surrounding patches without access to the original or reconstructed image data. Encoding and decoding can therefore be performed in parallel. We demonstrate that BINet improves the compression quality of a competitive deep image codec across a range of compression levels.

最近的深度学习模型优于标准的有损图像压缩编解码器。但是，在逐个补丁的基础上应用这些模型需要对每个图像补丁进行独立的编码和解码。因此丢失了来自相邻补丁的影响，从而导致了低比特率的块伪像。我们提出了二进制修补网络（BINet），它是一种自动编码器框架，该框架结合了二进制修补以恢复相邻补丁之间的相互依赖性，以改进基于补丁的静止图像压缩。在解码补丁时，BINet还会使用周围补丁的二进制编码来指导其重建。与顺序修补方法不同，在顺序修补方法中，补丁是根据先前的重建进行解码的，BINet直接对周围补丁的二进制代码进行操作，而无需访问原始或重建的图像数据。因此，可以并行执行编码和解码。我们证明BINet在一系列压缩级别上提高了竞争性深层图像编解码器的压缩质量。