Compression technology is essential for efficient image transmission and
storage. With the rapid advances in deep learning, images are beginning to be
used for image recognition as well as for human vision. For this reason,
research has been conducted on image coding for image recognition, and this
field is called Image Coding for Machines (ICM). There are two main approaches
in ICM: the ROI-based approach and the task-loss-based approach. The former
approach has the problem of requiring an ROI-map as input in addition to the
input image. The latter approach has the problems of difficulty in learning the
task-loss, and lack of robustness because the specific image recognition model
is used to compute the loss function. To solve these problems, we propose an
image compression model that learns object regions. Our model does not require
additional information as input, such as an ROI-map, and does not use
task-loss. Therefore, it is possible to compress images for various image
recognition models. In the experiments, we demonstrate the versatility of the
proposed method by using three different image recognition models and three
different datasets. In addition, we verify the effectiveness of our model by
comparing it with previous methods.

通过深度学习，我们提出了一种学习对象区域的图像压缩模型，它不需要额外的信息作为输入，并且可以适用于不同的图像识别模型。

基于目标区域学习的机器图像编码

Image Coding for Machines with Object Region Learning

Image Coding for Machines (ICM) aims to compress images for AI tasks analysis
rather than meeting human perception. Learning a kind of feature that is both
general (for AI tasks) and compact (for compression) is pivotal for its
success. In this paper, we attempt to develop an ICM framework by learning
universal features while also considering compression. We name such features as
omnipotent features and the corresponding framework as Omni-ICM. Considering
self-supervised learning (SSL) improves feature generalization, we integrate it
with the compression task into the Omni-ICM framework to learn omnipotent
features. However, it is non-trivial to coordinate semantics modeling in SSL
and redundancy removing in compression, so we design a novel information
filtering (IF) module between them by co-optimization of instance
distinguishment and entropy minimization to adaptively drop information that is
weakly related to AI tasks (e.g., some texture redundancy). Different from
previous task-specific solutions, Omni-ICM could directly support AI tasks
analysis based on the learned omnipotent features without joint training or
extra transformation. Albeit simple and intuitive, Omni-ICM significantly
outperforms existing traditional and learning-based codecs on multiple
fundamental vision tasks.

本文提出了一种名为 Omni-ICM 的 Image Coding for Machines 框架，通过自我监督学习和信息筛选模块，学习了通用且紧凑的全能特征，可直接用于基于学习的编解码器支持人工智能任务分析。与传统编码器和基于学习的编解码器相比，Omni-ICM 在多个基础视觉任务上表现出更好的性能。

全能特征学习的机器图像编码

Image Coding for Machines with Omnipotent Feature Learning

The past decades have witnessed the rapid development of image and video
coding techniques in the era of big data. However, the signal fidelity-driven
coding pipeline design limits the capability of the existing image/video coding
frameworks to fulfill the needs of both machine and human vision. In this
paper, we come up with a novel image coding framework by leveraging both the
compressive and the generative models, to support machine vision and human
perception tasks jointly. Given an input image, the feature analysis is first
applied, and then the generative model is employed to perform image
reconstruction with features and additional reference pixels, in which compact
edge maps are extracted in this work to connect both kinds of vision in a
scalable way. The compact edge map serves as the basic layer for machine vision
tasks, and the reference pixels act as a sort of enhanced layer to guarantee
signal fidelity for human vision. By introducing advanced generative models, we
train a flexible network to reconstruct images from compact feature
representations and the reference pixels. Experimental results demonstrate the
superiority of our framework in both human visual quality and facial landmark
detection, which provide useful evidence on the emerging standardization
efforts on MPEG VCM (Video Coding for Machine).

本文介绍了一种新的图像编码方案，其通过利用压缩模型和生成模型共同支持机器视觉和人的视觉感知任务，并使用紧凑边缘映射连接两种视觉模式，实现了对图像特征的重新构建，其提供了有用的证据支持 MPEG VCM（面向机器的视频编码）标准化工作。