This fact sheet describes our proposed method for the video track of Challenge on Learned Image Compression (CLIC) 2024. Our scheme follows the typical hybrid coding framework with advanced techniques in motion estimation, context mining, and spatial-temporal super-resolution to enhance rate-distortion performance, particularly at low bitrates.

Remote medical diagnosis has emerged as a critical and indispensable technique in practical medical systems, where medical data are required to be efficiently compressed and transmitted for diagnosis by either professional doctors or intelligent diagnosis devices. In this process, a large amount of redundant content irrelevant to the diagnosis is subjected to high-fidelity coding, leading to unnecessary transmission costs. To mitigate this, we propose diagnosis-oriented medical image compression, a special semantic compression task designed for medical scenarios, targeting to reduce the compression cost without compromising the diagnosis accuracy. However, collecting sufficient medical data to optimize such a compression system is significantly expensive and challenging due to privacy issues and the lack of professional annotation. In this study, we propose DMIC, the first efficient transfer learning-based codec, for diagnosis-oriented medical image compression, which can be effectively optimized with only few-shot annotated medical examples, by reusing the knowledge in the existing reinforcement learning-based task-driven semantic coding framework, i.e., HRLVSC [1]. Concretely, we focus on tuning only the partial parameters of the policy network for bit allocation within HRLVSC, which enables it to adapt to the medical images. In this work, we validate our DMIC with the typical medical task, Coronary Artery Segmentation. Extensive experiments have demonstrated that our DMIC can achieve 47.594%BD-Rate savings compared to the HEVC anchor, by tuning only the A2C module (2.7% parameters) of the policy network with only 1 medical sample.

In recent years, various mega-constellation plans have been issued and the number of planned satellites has been increasing. However, the boundary of satellite number that existing tracking, telemetry and command (TT&C) resources can support has never been analyzed. This paper discusses TT&C capacity of mega-constellations for the first time. Under the requirement of instantaneity and reliability of TT&C system, the boundary of constellation satellites number that can be supported based on the inter-satellite link is explored. We first establish the satellite TT&C model based on N*D/D/1 queuing model. Then we analyze the relationship between satellites number, waiting delay and package loss probability under normal, compressed and abnormal telemetry transmission scenarios. Finally, the results of theoretical analysis, numerical and Monte Carlo simulation are given. Results show that the networked TT&C system with inter-satellite link will greatly increase the number of satellites that can be supported.

We have witnessed the rapid development of learned image compression (LIC). The latest LIC models have outperformed almost all traditional image compression standards in terms of rate-distortion (RD) performance. However, the time complexity of LIC model is still underdiscovered, limiting the practical applications in industry. Even with the acceleration of GPU, LIC models still struggle with long coding time, especially on the decoder side. In this paper, we analyze and test a few prevailing and representative LIC models, and compare their complexity with traditional codecs including H.265/HEVC intra and H.266/VVC intra. We provide a comprehensive analysis on every module in the LIC models, and investigate how bitrate changes affect coding time. We observe that the time complexity bottleneck mainly exists in entropy coding and context modelling. Although this paper pay more attention to experimental statistics, our analysis reveals some insights for further acceleration of LIC model, such as model modification for parallel computing, model pruning and a more parallel context model.