Perceptual video quality optimization in AWGN channel using low complexity channel code rate allocation

ABSTRACT In error-prone channels, forward error correction is necessary for protecting important data. In this paper, we use a packet loss visibility model to evaluate the visual importance of video packets to be transmitted. With the loss visibility of each packet, we use the Branch and Bound method to optimally allocate rates of Rate-Compatible Punctured Convolutional codes. The complexity of our prior algorithm can be reduced by k-means clustering before using the Branch and Bound method. Experimental results show that the proposed unequal error protection algorithm can improve upon the received video quality compared to our prior work with much lower complexity. Index Terms-Unequal error protection, packet loss visibility model, perceptual quality

Robust Deep Sensing Through Transfer Learning in Cognitive Radio

We propose a robust spectrum sensing framework based on deep learning. The received signals at the secondary user's receiver are filtered, sampled and then directly fed into a convolutional neural network. Although this deep sensing is effective when operating in the same scenario as the collected training data, the sensing performance is degraded when it is applied in a different scenario with different wireless signals and propagation. We incorporate transfer learning into the framework to improve the robustness. Results validate the effectiveness as well as the robustness of the proposed deep spectrum sensing framework

Cross layer resource allocation design for uplink video OFDMA wireless systems

Abstract-We study an uplink video communication system with multiple users in a centralized wireless cell. The multiple access scheme is Orthogonal Frequency Division Multiple Access (OFDMA). Both physical layer channel state information (CSI) and application layer rate distortion (RD) information of video streams are collected by the base station. With the goal of minimizing the average video distortion across all the users in the system, we design an iterative resource allocation algorithm for subcarrier assignment and power allocation. Based on the physical layer resource allocation decision, the user will adapt the application layer video source coding rate. To show the advantage of this cross layer algorithm, numerical results are compared with two baseline resource allocation algorithms using only physical layer information or only application layer information. Bit-level simulation results are presented which take into account the imperfection of the video coding rate control, as well as channel errors. Index Terms-Cross layer design, multiuser video communications system, OFDMA, video multiplexing

Vector quantization of image subbands: a survey

Subband and wavelet decompositions are powerful tools in image coding because of their decorrelating effects on image pixels, the concentration of energy in a few coefficients, their multirate/multiresolution framework, and their frequency splitting, which allows for efficient coding matched to the statistics of each frequency band and to the characteristics of the human visual system. Vector quantization (VQ) provides a means of converting the decomposed signal into bits in a manner that takes advantage of remaining inter and intraband correlation as well as of the more flexible partitions of higher dimensional vector spaces. Since 1988, a growing body of research has examined the use of VQ for subband/wavelet transform coefficients. We present a survey of these methods

Detecting Generated Images by Real Images Only

As deep learning technology continues to evolve, the images yielded by generative models are becoming more and more realistic, triggering people to question the authenticity of images. Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This learning paradigm will result in efficiency and generalization issues, making detection methods always lag behind generation methods. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace. As a result, images from different generative models can be detected, solving some long-existing problems in the field. Experimental results show that although our method was trained only by real images and uses 99.9\% less training data than other deep learning-based methods, it can compete with state-of-the-art methods and shows excellent performance in detecting emerging generative models with high inference efficiency. Moreover, the proposed method shows robustness against various post-processing. These advantages allow the method to be used in real-world scenarios

Device-to-Device Assisted Video Transmission

To increase spectrum efficiency, researchers envi- sion a device-to-device (D2D) communication system in which a closely located mobile device pair may share the same spectrum with a cellular user. By opportunistically choosing the frequency, the D2D pair may increase the spectrum efficiency in terms of data rate per Hertz, at the price of additional interference to that cellular user. In previous models, users either stop cellular transmission and switch to D2D transmission or vice versa. However, if the cell is fully loaded, a D2D pair will not be able to switch back to the conventional mode because no extra resource is available. In this paper, we propose a D2D assisted model, where a D2D link is enabled to assist transmission, while keeping the conventional cellular transmission. In this way, the D2D link can be turned on and off according to the link quality. We also propose a PHY-layer study for the transmission scheme in such a way that the system throughput and the video reception quality is always improved compared to a conventional link

Joint Source-Channel Coding and Unequal Error Protection for Video Plus Depth

Abstract-We consider the joint source-channel coding (JSCC) problem of 3-D stereo video transmission in video plus depth format over noisy channels. Full resolution and downsampled depth maps are considered. The proposed JSCC scheme yields the optimum color and depth quantization parameters as well as the optimum forward error correction code rates used for unequal error protection (UEP) at the packet level. Different coding scenarios are compared and the UEP gain over equal error protection is quantified for flat Rayleigh fading channels. Index Terms-3-D video, joint source-channel coding, unequal error protection, video plus depth