Recently deep learning-based image compression methods have achieved significant achievements and gradually outperformed traditional approaches including the latest standard Versatile Video Coding (VVC) in both PSNR and MS-SSIM metrics. Two key components of learned image compression are the entropy model of the latent representations and the encoding/decoding network architectures. Various models have been proposed, such as autoregressive, softmax, logistic mixture, Gaussian mixture, and Laplacian. Existing schemes only use one of these models. However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions within one image. In this paper, we propose a more flexible discretized Gaussian-Laplacian-Logistic mixture model (GLLMM) for the latent representations, which can adapt to different contents in different images and different regions of one image more accurately and efficiently, given the same complexity. Besides, in the encoding/decoding network design part, we propose a concatenated residual blocks (CRB), where multiple residual blocks are serially connected with additional shortcut connections. The CRB can improve the learning ability of the network, which can further improve the compression performance. Experimental results using the Kodak, Tecnick-100 and Tecnick-40 datasets show that the proposed scheme outperforms all the leading learning-based methods and existing compression standards including VVC intra coding (4:4:4 and 4:2:0) in terms of the PSNR and MS-SSIM. The source code is available at https://github.com/fengyurenpingsheng.

Hyperspectral (HS) pansharpening aims to create a pansharpened image that integrates the spatial details of the panchromatic (PAN) image and the spectral content of the HS image. In this article, we present a deep convolutional network within the mature Gaussian–Laplacian pyramid for pansharpening (LPPNet). The overall structure of LPPNet is a cascade of the Laplacian pyramid dense network with a similar structure at each pyramid level. Following the general idea of multiresolution analysis (MRA), the subband residuals of the desired HS images are extracted from the PAN image and injected into the upsampled HS image to reconstruct the high-resolution HS images level by level. Applying the mature Laplace pyramid decomposition technique to the convolution neural network (CNN) can simplify the pansharpening problem into several pyramid-level learning problems so that the pansharpening problem can be solved with a shallow CNN with fewer parameters. Specifically, the Laplacian pyramid technology is used to decompose the image into different levels that can differentiate large- and small-scale details, and each level is handled by a spatial subnetwork in a divide-and-conquer way to make the network more efficient. Experimental results show that the proposed LPPNet method performs favorably against some state-of-the-art pansharpening methods in terms of objective indexes and subjective visual appearance.

Recently, deep learning-based image compression has made significant progresses, and has achieved better rate-distortion (R-D) performance than the latest traditional method, H.266/VVC, in both MS-SSIM metric and the more challenging PSNR metric. However, a major problem is that the complexities of many leading learned schemes are too high. In this paper, we propose an efficient and effective image coding framework, which achieves similar R-D performance with lower complexity than the state of the art. First, we develop an improved multi-scale residual block (MSRB) that can expand the receptive field and capture global information more efficiently, which further reduces the spatial correlation of the latent representations. Second, an importance scaling network is introduced to directly scale the latents to achieve content-adaptive bit allocation without sending side information, which is more flexible than previous importance map methods. Third, we apply a post-quantization filter (PQF) to reduce the quantization error, motivated by the Sample Adaptive Offset (SAO) filter in video coding. Moreover, our experiments show that the performance of the system is less sensitive to the complexity of the decoder. Therefore, we design an asymmetric paradigm, in which the encoder employs three stages of MSRBs to improve the learning capacity, whereas the decoder only uses one stage of MSRB, which reduces the decoder complexity and still yields satisfactory performance. Experimental results show that compared to the state-of-the-art method, the encoding and decoding time of the proposed method are about 17 times faster, and the R-D performance is only reduced by about 1% on both Kodak and Tecnick-40 datasets, which is still better than H.266/VVC(4:4:4) and other leading learning-based methods. Our source code is publicly available at https://github.com/fengyurenpingsheng.

Intelligent reflecting surface (IRS) has emerged as a potential technology to achieve smart wireless communications and high energy efficiency. On the other hand, nonorthogonal multiple access (NOMA)-enabled backscatter communications have shown a great potential in large-scale Internet of Things (IoT) networks. In this article, we consider a downlink IRS-assisted backscatter communication with NOMA. We further consider a two-user scenario with channel disparity from the base station. We first derive the probability density function of the sum of the modulus of reflected channels, where each channel follows the Rayleigh distribution with dissimilar variances. The respective and generalized closed-form outage probability expressions are derived for the considered scenario. Simulation results validate the accuracy of the analytical outage probability expressions. We demonstrate that the far user can achieve a superior performance with the increase of reflecting elements or the reflection coefficients.

Recently deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networks for multiple bit rates, which increase the implementation complexity. In this paper, we propose a new variable-rate image compression framework, which employs generalized octave convolutions (GoConv) and generalized octave transposed-convolutions (GoTConv) with built-in generalized divisive normalization (GDN) and inverse GDN (IGDN) layers. Novel GoConv- and GoTConv-based residual blocks are also developed in the encoder and decoder networks. Our scheme also uses a stochastic rounding-based scalar quantization. To further improve the performance, we encode the residual between the input and the reconstructed image from the decoder network as an enhancement layer. To enable a single model to operate with different bit rates and to learn multi-rate image features, a new objective function is introduced. Experimental results show that the proposed framework trained with variable-rate objective function outperforms the standard codecs such as H.265/HEVC-based BPG and state-of-the-art learning-based variable-rate methods.

The Peak Signal-to-Noise Ratio (PSNR) has been widely employed as an objective Video Quality Assessment (VQA) metric. Usually, videos are represented in the YCbCr color space, which results in three PSNR values for each video frame. Several VQA metrics have been proposed to measure the video quality with a single combined PSNR. However, these metrics are derived heuristically without theoretical justification. In this paper, based on our extensive subjective tests on the sensitivity of the human visual system to different color components, we derive the optimal weighting coefficients of a color-sensitivity-based combined PSNR (CSPSNR). Moreover, to verify the performance of the combined PSNR, test sequences with different levels of combined PSNRs are used to evaluate the quality of the videos. However, no such database is currently available for measuring the effectiveness of different methods regarding combined PSNRs. In this paper, we design a novel coding scheme to produce sequences whose PSNRs are the combinations of different levels of PSNRs of YCbCr, with which the correlation between the subjective score and the combined PSNR is analyzed. Experiment results and statistical analysis demonstrate that the proposed CSPSNR correlates better with the mean opinion score (MOS) than existing methods.