论文标题
通过深层编辑更好的压缩
Better Compression with Deep Pre-Editing
论文作者
论文摘要
我们可以通过标准编解码器压缩图像，同时避免可见的伪影？答案是显而易见的 - 只要钻头预算足够大，这是可行的。如果压缩的分配的位量不足怎么办？不幸的是，人工制品是生活的事实。多年来，许多尝试以各种成功的程度来与这种现象作斗争。在这项工作中，我们旨在打破比特率和图像质量之间的邪恶联系，并提出一种方法来通过预先编辑传入的图像并修改其内容以适合给定位来规避压缩工件。我们将此编辑操作设计为学习的卷积神经网络，并为其培训制定了优化问题。我们的损失考虑了原始图像和编辑图像之间的邻近性，对所提出的图像进行了一些预算的惩罚，以及一种无引用的图像质量度量，以迫使结果在视觉上令人愉悦。提出的方法在流行的JPEG压缩上证明了这一方法，显示了碎屑的节省和/或具有复杂的编辑效果获得的视觉质量的改进。
Could we compress images via standard codecs while avoiding visible artifacts? The answer is obvious -- this is doable as long as the bit budget is generous enough. What if the allocated bit-rate for compression is insufficient? Then unfortunately, artifacts are a fact of life. Many attempts were made over the years to fight this phenomenon, with various degrees of success. In this work we aim to break the unholy connection between bit-rate and image quality, and propose a way to circumvent compression artifacts by pre-editing the incoming image and modifying its content to fit the given bits. We design this editing operation as a learned convolutional neural network, and formulate an optimization problem for its training. Our loss takes into account a proximity between the original image and the edited one, a bit-budget penalty over the proposed image, and a no-reference image quality measure for forcing the outcome to be visually pleasing. The proposed approach is demonstrated on the popular JPEG compression, showing savings in bits and/or improvements in visual quality, obtained with intricate editing effects.
