Deep learning technologies have been popular in the image compression field for some time. An increasing number of deep-learning-based models are proposed to improve Rate-Distortion (RD) performance. Previous algorithms are implemented in the specific platform and can not be applied in cross-platform environments. In this paper, we present an open-source algorithm library called OpenDIC, which integrates a variety of end-to-end image compression methods in cross-platform environments. The contribution and details of the algorithms used in the library are described. To evaluate the performance of these algorithms, we conduct a comprehensive performance test. We compare and analyze each algorithm according to RD performance, running time, and GPU memory occupancy. The algorithm library has been released at https://openi.pcl.ac.cn/OpenDIC/.

Video streaming has become an essential component of our everyday routines. Nevertheless, video data imposes a significant strain on data usage, demanding substantial bandwidth and storage resources for effective transmission. To suit explosively increasing video transmission and storage requirements, deep-learning-based video compression has developed rapidly in the past few years. New methods have mushroomed in order to achieve better Rate-Distortion (RD) performance. However, the absence of an algorithm library that can effectively sort, classify, and conduct extensive benchmark testing on existing algorithms remains a challenge. In this paper, we present an open-source algorithm library called OpenDMC, which integrates a variety of end-to-end video compression methods in cross-platform environments. We provide comprehensive descriptions of the algorithms used in the library, including their contributions and implementation details. We perform a thorough benchmarking test to evaluate the performance of the algorithms. We meticulously compare and analyze each algorithm based on various metrics, including RD performance, running time, and GPU memory usage. The open-source library for OpenDMC is available at https://openi.pcl.ac.cn/OpenDMC/.

Face age transformation aims to generate a face image in the past or future and has been receiving increasing attention for its significance recently. This paper proposes a local-global interactive framework for long-span face age transformation. We divide a face image into five local parts and design a local generative network for each of them to learn the local changes of a face image. Meanwhile a global generative network is utilized to learn the global changes. We introduce an interactive network and an age classification network, which are respectively used to integrate the local and global features and maintain the corresponding age features in different age groups. Given a face image at a certain age, our network can produce a realistic image of face aging or rejuvenation. We test and evaluate the model on complex datasets. Extensive qualitative comparison experiments prove the effectiveness and potential of our proposed method.

Depth completion is becoming a particularly important yet challenging problem with the growingly rapid progress of depth sensing technologies. Depth completion aims to complete sparse and noisy depth images to generate dense depth images. In this paper, we propose a multiscale adaptation fusion network (MAFN) for depth completion. The depth features are fused with RGB features at multiple scales with adaptation modules, where a neighbour attention mechanism is designed to adapt the local structures of the RGB image and the depth image. The fusion and completion process are unified under the encoder-decoder framework which is learned in an end-to-end way. By exploiting the detailed structural relationships of RGB images and depth images, our MAFN model can accurately complete and restore the invalid depth values on the sparse depth images. We test the proposed method on the challenging KITTI depth completion benchmark. The experimental results prove the effectiveness and strength of the proposed method.

Compressed video action recognition has drawn growing attention for the storage and processing advantages of compressed videos over original raw videos. While the past few years have witnessed remarkable progress in this problem, most existing approaches rely on RGB frames from raw videos and require multi-step training. In this paper, we propose a novel Slow-I-Fast-P (SIFP) neural network model for compressed video action recognition. It consists of the slow I pathway receiving a sparse sampling I-frame clip and the fast P pathway receiving a dense sampling pseudo optical flow clip. An unsupervised estimation method and a new loss function are designed to generate pseudo optical flows in compressed videos. Our model eliminates the dependence on the traditional optical flows calculated from raw videos. The model is trained in an end-to-end way. The proposed method is evaluated on the challenging HMDB51 and UCF101 datasets. The extensive comparison results and ablation studies demonstrate the effectiveness and strength of the proposed method.