论文标题
JQF：最佳JPEG量化表融合通过模拟纹理图像并预测纹理
JQF: Optimal JPEG Quantization Table Fusion by Simulated Annealing on Texture Images and Predicting Textures
论文作者
论文摘要
近三十年来，JPEG一直是广泛使用的有损图像压缩编解码器。 JPEG标准允许使用自定义量化表；但是，在可接受的计算成本中找到最佳量化表仍然是一个具有挑战性的问题。这项工作试图通过引入新的纹理镶嵌图像概念来解决计算成本和特定图像特定最优性之间平衡的困境。模拟退火技术没有优化单个图像或代表性图像的集合，而是将其应用于纹理镶嵌图像，以搜索每个纹理类别的最佳量化表。我们使用预训练的VGG-16 CNN模型来学习这些纹理功能并预测新图像的纹理分布，然后融合最佳纹理表，以配备图像特定的最佳量化表。在具有质量设置$ Q = 95 $的柯达数据集上，我们的实验显示，尺寸比JPEG标准表的尺寸降低了23.5％，而FSIM略有下降，这在视觉上是不可感知的。所提出的JQF方法可实现jpeg编码的每个图像最优性，并以不到一秒钟的额外定时成本来实现。在线演示可从https://matthorn.s3.amazonaws.com/jqf/qtbl_vis.html获得
JPEG has been a widely used lossy image compression codec for nearly three decades. The JPEG standard allows to use customized quantization table; however, it's still a challenging problem to find an optimal quantization table within acceptable computational cost. This work tries to solve the dilemma of balancing between computational cost and image specific optimality by introducing a new concept of texture mosaic images. Instead of optimizing a single image or a collection of representative images, the simulated annealing technique is applied to texture mosaic images to search for an optimal quantization table for each texture category. We use pre-trained VGG-16 CNN model to learn those texture features and predict the new image's texture distribution, then fuse optimal texture tables to come out with an image specific optimal quantization table. On the Kodak dataset with the quality setting $Q=95$, our experiment shows a size reduction of 23.5% over the JPEG standard table with a slightly 0.35% FSIM decrease, which is visually unperceivable. The proposed JQF method achieves per image optimality for JPEG encoding with less than one second additional timing cost. The online demo is available at https://matthorn.s3.amazonaws.com/JQF/qtbl_vis.html
