Intelligent reflective surface (IRS) assisted symbiotic radio (SR) network has been proposed as a promising solution for the sixth generation (6G) mobile wireless system, which achieves mutualistic spectrum sharing and highly reliable backscattering communication with extremely low energy cost. On the other hand, exponential growth in video traffic makes wireless video transmission more challenging in the 6G era. With the assistance of IRS based secondary link in SR network, an efficient soft video transmission scheme (IRSCast) is proposed to achieve linear quality transition under the drastically varying wireless channel. To minimize the transmission distortion of the video signal, a multivariable optimization problem is formulated to jointly optimize the wireless resources, including transmission power, active beamforming of the primary transmitter (PTx), and passive beamforming of the secondary transmitter (STx). Then, an alternating optimization method is utilized to decouple the multivariate optimization problem into multiple univariate sub-problems that are finally solved by semi-positive definite relaxation and Lagrange multiplier methods. The simulation results demonstrated that the proposed IRSCast method significantly improves the objective and subjective quality of the received video.

The CNN-based end-to-end learned image compression methods have already achieved a significant improvement in terms of coding efficiency. Moreover, with the capability of modeling long-range global correlation, the transformer-based image compression has further elevated the coding efficiency to outperform the latest Versatile Video Coding (VVC) standard. Nonetheless, the high computational burden of the self-attention mechanism in the transformer design present a significant obstacle for practical applications. To address this concern, we propose a relatively low-complexity end-to-end learned image compression approach by integrating an efficient lightweight attention module, which effectively mitigates the computational overhead associated with self-attention in the transformer design. The experimental results demonstrate that the proposed method achieves better RD performance than VVC. Furthermore, as compared to the prevailing state-of-the-art transformer-based approach, our method accelerates the coding speed by over 6 times while maintaining comparable coding efficiency.

The success of convolutional neural network (CNN) based single image dynamic scene blind deblurring (SIDSBD) methods mainly stems from the multi-scale/multi-patch model and the designs of the encoder-decoder architecture, and the residual block structure, which make different contributions to SIDSBD. In this paper, we further exploit the advantages of the multi-scale model, the encoder-decoder module, and the residual block structure, respectively, and propose a novel multi-scale channel attention network (MSCAN) for effective single image dynamic scene blind deblurring. Different from existing multi-scale models, in our proposed network, each scale consists of multiple levels, in which a novel spatial pyramid pooling channel attention (SPPCA) strategy is proposed to adaptively rescale the channel-wise features by using both the global and local feature statistics for more powerful network representation. Extensive experiments on both the synthetic benchmark datasets and the real blurred images show that our method can produce better deblurring results than the state-of-the-art SIDSBD methods in terms of both qualitative evaluation and quantitative metrics.

Tracking of vehicles is a key technique for Intelligent transportation system, which commonly follows tracking-by-detection strategy. Due to high appearance similarity among vehicles and heavy occlusion caused by busy traffic flow, a major challenge in such a tracking system is the limited performance of the underlying detector which may produce noisy detections. Consequently, Siamese network and backward prediction-based vehicle tracking approach is proposed. Siamese network based forward position prediction is designed to alleviate the interference of noisy detections, while backward prediction verification is performed to reduce the false positives arising with forward prediction. The final tracklets are obtained through weighted merging based on the detection confidence and forward prediction confidence. The experiment results demonstrate that the proposed method outperforms the state-of-the-art on the UA-DETRAC vehicle tracking dataset, as well as maintains real-time processing at an average tracking speed of 20.1fps, which can be used for real-time applications.

Recently, a novel uncoded (pseudoanalog) scheme called SoftCast is proposed for wireless video transmission, which eliminates the cliff effect of the state-of-the-art source-channel coding based schemes and achieves linear quality transition within a wide range of channel signal-to-noise ratio. Therefore, SoftCast-like uncoded and hybrid transmission has become an attractive research issue for natural 2-D video. However, very few studies focus on the SoftCast-based wireless transmission of the 3-D video (3DV) currently. One critical issue of 3DV SoftCast is how to allocate the limited power budget of the transmitter to the texture videos and depth maps of the 3DV to achieve the optimal overall quality on the receiver side, including the transmission quality of the reference views and the synthesis quality of the virtual views. This paper attempts to solve the optimal joint power allocation problem in an efficient way. First, we formulate the target problem as a constrained power-distortion optimization (PDO) problem mathematically. Then, each part of the distortion is analyzed and formulated in a closed form. Finally, the PDO problem is mapped to an unconstrained convex optimization problem and solved by the Lagrangian multiplier method. Simulation results demonstrate that the performance of the proposed method is close to that of the full search method, which can provide the best performance theoretically. Nevertheless, the complexity of the proposed method is negligible compared with that of the full search method. In addition, as compared with the fixed ratio (e.g., 1:1) power allocation between texture and depth, the proposed method can achieve a PNSR gain up to 1.8 dB.

Transmitting depth images along with the corresponding textures enables a wide range of receiver-side 3D applications. Since each pixel on the depth images represents a corresponding 3D scene geometric information, when compressed during transmission the compression artifacts will lead to severe geometry distortions and visual perceptual degradation. To solve this problem, in this paper we proposed a convolutional neural network (CNN) cascade for suppressing the compression artifacts on depth images. According to the feature of depth images, we furthermore, adopt a weighted loss function for network training which can adaptively improve the learning efficiency and accuracy. Meanwhile, in order to overcome the limited training data problem, we audaciously trained our network on textures first and then finetune on the target depth images. To our best knowledge, few works have applied CNN on depth images targeting for compression artifacts reduction (CAR). Through extensive experiments, our proposed solution achieves higher quality for both reconstructed depth images and synthesized virtual views than the state-of-the-art methods.