JQF：基于模拟退火的纹理图像最优JPEG Quantization表融合及纹理预测
JQF: Optimal JPEG Quantization Table Fusion by Simulated Annealing on Texture Images and Predicting Textures
摘要：
JPEG是一种广泛使用的有损图像压缩编解码器近三十年。JPEG标准允许使用定制的量化表，然而，在可接受的计算成本内找到最佳量化表仍然是一个具有挑战性的问题。本文通过引入纹理拼接图像的新概念，试图解决计算成本和图像特定最优性之间的平衡困境。代替优化单个图像或代表性图像的集合，将模拟退火技术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特征并预测新图像的纹理分布，然后融合最佳纹理表以得到图像特定的最佳量化表。在Kodak数据集上，质量设置为$Q=95$，我们的实验显示，与JPEG标准表相比，大小减少了23.5%，FSIM略微减少了0.35%，这在视觉上是不可感知的。所提出的JQF方法实现了JPEG编码的每幅图像的最优性，其额外的时间成本小于一秒。在线演示可在https://matthorn.s3.amazonaws.com/JQF/qtbl_vis.html上获得
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
