Decoding human movement intentions from electroencephalography (EEG) signals is critical for brain-computer interface (BCI) applications in motor neurorehabilitation, active assistance, and functional augmentation. However, current BCI models face two challenges for motor-impaired patients: 1) prolonged EEG data collection from patients is difficult; 2) differences in brain functional structures and motor behaviors between healthy individuals and patients limit the generalizability of models trained on healthy individuals' EEG data. To address these challenges, this study proposes a transfer learning-based model, TL-ME, to bridge the gap between healthy individuals' and patients' EEG data and improve movement decoding accuracy for patients. TL-ME integrates an attention-based feature extractor, adversarial domain discriminator, multi-source selection, and movement classifier to transfer knowledge from healthy individuals' EEG data (source domain) to patients' EEG data (target domain). Temporal and spectral visualizations are used to inspect brain activation patterns for shared motor tasks between healthy individuals and patients. Experimental results show a 10.8% improvement in upper-limb movement decoding's accuracy using TL-ME, with each module contributing to performance gains. Visualization analyses also demonstrate similar brain activation patterns across domains, validating the transferability of healthy individuals' EEG data to patient-specific models. This work introduces a novel cross-population transfer learning approach that leverages healthy individuals' EEG data to enhance neural decoding for motor-impaired patients, bridging the gap between experimental studies and real-world applications in BCI-based neurorehabilitation.

The pervasive computing environment and wide network bandwidth provide users more opportunities to share screen content among multiple devices. In this article, we introduce a remote display system to enable screen sharing among multiple devices with high fidelity and responsive interaction. In the developed system, the frame-level screen content is compressed and transmitted to the client side for screen sharing, and the instant control inputs are simultaneously transmitted to the server side for interaction. Even if the screen responds immediately to the control messages and updates at a high frame rate on the server side, it is difficult to update the screen content with low delay and high frame rate in the client side due to non-negligible time consumption on the whole screen frame compression, transmission, and display buffer updating. To address this critical problem, we propose a layered structure for screen coding and rendering to deliver diverse screen content to the client side with an adaptive frame rate. More specifically, the interaction content with small region screen update is compressed by a blockwise screen codec and rendered at a high frame rate to achieve smooth interaction, while the natural video screen content is compressed by standard video codec and rendered at a regular frame rate for a smooth video display. Experimental results with real applications demonstrate that the proposed system can successfully reduce transmission bandwidth cost and interaction delay during screen sharing. Especially for user interaction in small regions, the proposed system can achieve a higher frame rate than most previous counterparts.

With the development of depth data acquisition technologies, access to high-precision depth with more than 8-b depths has become much easier and determining how to efficiently represent and compress high-precision depth is essential for practical depth storage and transmission systems. In this paper, we propose a layered high-precision depth compression framework based on an 8-b image/video encoder to achieve efficient compression with low complexity. Within this framework, considering the characteristics of the high-precision depth, a depth map is partitioned into two layers: 1) the most significant bits (MSBs) layer and 2) the least significant bits (LSBs) layer. The MSBs layer provides rough depth value distribution, while the LSBs layer records the details of the depth value variation. For the MSBs layer, an error-controllable pixel domain encoding scheme is proposed to exploit the data correlation of the general depth information with sharp edges and to guarantee the data format of LSBs layer is 8 b after taking the quantization error from MSBs layer. For the LSBs layer, standard 8-b image/video codec is leveraged to perform the compression. The experimental results demonstrate that the proposed coding scheme can achieve real-time depth compression with satisfactory reconstruction quality. Moreover, the compressed depth data generated from this scheme can achieve better performance in view synthesis and gesture recognition applications compared with the conventional coding schemes because of the error control algorithm.

In this paper, we propose a high frame rate screen video compression scheme aiming at improving the interactive user experience on screen sharing applications. The proposed screen video compression is performed as two-layer coding: a base layer coding using the conventional video codec and an enhancement layer coding using the proposed open-loop coding scheme. For efficient frame level layer selection and compression, the content update of each frame is evaluated through global motion detection. The screen frame with significant content update is fed to the conventional video encoder in base layer. In contrast, the frame with little update is compressed in enhancement layer in which the duplicate content is indicated by global motion vector and skip flag while the updated content is encoded by distinct intra modes in terms of inherent local features. The experimental results demonstrate that for the screen video containing interaction, the proposed coding scheme can achieve 3.09ms/frame encoding rate and 2.33ms/frame decoding rate with efficient rate distortion performance.

In this paper, we propose a layered screen video coding scheme based on existing video codecs to leverage hardware video codec for efficient screen video compression. In this scheme, the screen video compression is performed as two-layer coding: base layer coding and enhancement layer coding. The screen video is first analyzed in both frame and block levels for useful temporal and spatial information extraction to assist coding content selection in each layer. The non-skip screen frames are directly compressed by the conventional video codec in the base layer, while the screen contents sensitive to the video quality degradation are selected for improved coding in the enhancement layer. For contents to be enhanced, two intra coding modes are designed to improve the quality of the compressed text/graphics contents and suppress the artifacts introduced by chroma downsampling. The experimental results demonstrate that the screen video quality is improved objectively and subjectively by the proposed scheme with low cost on bitrate and computation complexity. Moreover, an average of 2.95dB coding gain is achieved in high bitrate.

Unlike traditional RGB video, Kinect-like depth is characterized by its large variation range and instability. As a result, traditional video compression algorithms cannot be directly applied to Kinect-like depth compression with respect to coding efficiency. In this paper, we propose a lossy Kinect-like depth compression framework based on the existing codecs, aiming to enhance the coding efficiency while preserving the depth features for further applications. In the proposed framework, the Kinect-like depth is reformed first by divisive normalized bilateral filter (DNBL) to suppress the depth noises caused by disparity normalization, and then block-level depth padding is implemented for invalid depth region compensation in collaboration with mask coding to eliminate the sharp variation caused by depth measurement failures. Before the traditional video coding, the inter-frame correlation of reformed depth is explored by proposed 2D+T prediction, in which depth volume is developed to simulate 3D volume to generate pseudo 3D prediction reference for depth uniqueness detection. The unique depth region, called active region is fed into the video encoder for traditional intra and inter prediction with residual coding, while the inactive region is skipped during depth coding. The experimental results demonstrate that our compression scheme can save 55%-85% in terms of bit cost and reduce coding complexity by 20%-65% in comparison with the traditional video compression algorithms. The visual quality of the 3D reconstruction is also improved after employing our compression scheme.

The emergence of Kinect facilitates the possibility of depth capture in real-time and with low cost by consumers. It also provides powerful tool and inspiration for researchers to engage in new array of technology development. However, the quality of the depth map captured from Kinect is still inadequate for many applications due to holes, noises and artifacts existing within the depth information. In this paper, we present a texture assisted Kinect depth inpainting framework, aiming at obtaining improved depth information. In this framework, the relationship between texture and depth is investigated, and the characteristics of depth are also exploited. More specifically, texture edge information is extracted to assist the depth inpainting. Furthermore, filtering and diffusion are designed for hole-filling and edge alignment. Experiment results demonstrate that the Kinect depth can be appropriately repaired in both smooth and edge region. Comparing with the original depth, the inpainted depth information enhances the quality of advanced processing such as 3D reconstruction.

With the rapid development of depth data acquisition technology, the high precision depth becomes much easier to access in real-time by depth sensors, and the generated high dynamic range (HDR) depth is widely adopted to benefit the depth assistant applications. Accordingly, the HDR depth compression becomes essential for the efficient depth storage and transmission. In this paper, we introduce a layered compression framework for HDR depth to achieve efficient and low-complexity depth compression. To leverage the state-of-art 8-bit image/video encoders, the HDR depth is partitioned into two layers: most significant bit (MSB) layer and least significant bit (LSB) layer. For MSB layer, an error controllable pixel domain encoding scheme is proposed to guarantee the compatibility for existing 8-bit codec by controlling quantization errors added back to LSB layer. Meanwhile, the efficient major color extraction and adaptive quantization enhance the coding performance of MSB layer. For LSB layer, the layer data with limited dynamic range is compressed by normal 8-bit image/video based encoding scheme. The experimental results demonstrate that our coding scheme can achieve real-time depth compression with the satisfactory reconstruction quality. The encoding time is less than 31ms/frame and the decoding time is around 20ms/frame in average. Our compression scheme can be easily integrated into the real-time depth transmission system.

Free viewpoint video (FVV) / Free viewpoint TV (FTV) on mobile devices over cellular networks is very challenging due to the requirement for large bandwidth and limitations in computation and battery life on mobile phones. To address such challenges, in this paper we propose a cloud-based FVV / FTV rendering framework for mobile devices over cellular networks. In this framework, cloud performs rendering for mobile devices. In order to achieve maximum QoE (Quality of Experience) for mobile users, we propose a novel resource allocation scheme, which jointly considers rendering allocation between cloud and client based on user's QoE and rate allocation among texture, depth, and channel rate based on rate-distortion analysis. We formulate this resource allocation scheme as an optimization problem which can then be transformed into a convex optimization for the given rate ratio. Experimental results demonstrate that the proposed cloud-based FVV rendering solution can substantially improve video quality on mobile devices comparing with traditional approaches.

Dynamic spectrum access has become a promising approach to fully utilize the scarce spectrum resources. In this paper, two new dynamic spectrum access schemes for secondary users are proposed, which are based on continuous-time Markov chains (CTMC). Both the interactions between primary and secondary users and the effects of imperfect spectrum sensing(i.e. miss-detection and false alarm) are considered in the schemes. Due to the fact that miss-detection would lead to collision between primary and secondary users, we derive the optimal access probabilities for secondary users, so that the QoS of primary users in terms of collision probability is guaranteed. Simulation results show that the proposed dynamic spectrum access approach under proportional fairness criterion achieves much higher fairness over max-throughput criterion. Moreover, the scheme with buffering mechanism can raise the channel occupancy remarkably.

Spectrum occupancy measurements are significant to obtain spectrum utilization, which will conduct the development and deployment of dynamic spectrum access technology. Threshold level setting is one of the most important parts in data processing. Most spectrum occupancy measurement methods adopt fixed threshold (such as experience value) to distinguish signal from noise. These methods can not be operated autonomously and sometimes has low accuracy. To solve the problems, we investigate the data processing technology in spectrum occupancy measurements. Particularly, an automated processing procedure is provided and an adaptive threshold method based on the analysis of measurement data's statistic characteristic is proposed to calculate the spectrum occupancy. Finally, experiments are performed to validate the adaptive threshold method with the data derived from the spectrum measurements of GSM UL/DL band in Beijing. The experiment results indicate that the proposed method realizes the selfadjusting of threshold level setting and decrease the workload of data processing effectively.