论文标题
基于对象的图像编码：学习驱动的重新访问
Object-Based Image Coding: A Learning-Driven Revisit
论文作者
论文摘要
大约二十年前对基于对象的图像编码（OBIC）进行了广泛研究，它承诺对超低比特率通信和高级语义内容理解具有广泛的应用，但由于具有任意形状的对象的效率低下，因此很少使用它。背后的一个基本问题是如何有效地以精细的粒度（例如特征元素或像素明智）处理任意形状的对象。为了攻击这一点，我们建议通过设计用于图像层分解的对象分割网络以及基于平行卷积的神经图像压缩网络来分别处理屏蔽前景对象和背景场景来应用元素掩盖和压缩。所有组件均在端到端的学习框架中进行了优化，以智能地权衡其（例如对象和背景）贡献以进行视觉愉悦的重建。我们进行了全面的实验，以评估Pascal VOC数据集的性能，以非常低的比特率方案（例如$ \ lyssim $ 0.1 $ 0.1 $ 0.1位 /像素-BPP），与JPEG2K，HEVC基于HEVC，基于HEVC的BPG和另一种博学的图像压缩方法相比，它们表现出明显的主观质量改进。所有相关材料均可在https://njuvision.github.io/neural-object-coding/上公开访问。
The Object-Based Image Coding (OBIC) that was extensively studied about two decades ago, promised a vast application perspective for both ultra-low bitrate communication and high-level semantical content understanding, but it had rarely been used due to the inefficient compact representation of object with arbitrary shape. A fundamental issue behind is how to efficiently process the arbitrary-shaped objects at a fine granularity (e.g., feature element or pixel wise). To attack this, we have proposed to apply the element-wise masking and compression by devising an object segmentation network for image layer decomposition, and parallel convolution-based neural image compression networks to process masked foreground objects and background scene separately. All components are optimized in an end-to-end learning framework to intelligently weigh their (e.g., object and background) contributions for visually pleasant reconstruction. We have conducted comprehensive experiments to evaluate the performance on PASCAL VOC dataset at a very low bitrate scenario (e.g., $\lesssim$0.1 bits per pixel - bpp) which have demonstrated noticeable subjective quality improvement compared with JPEG2K, HEVC-based BPG and another learned image compression method. All relevant materials are made publicly accessible at https://njuvision.github.io/Neural-Object-Coding/.
