In mainstream video encoders, rate control is crucial in scenarios with limited bandwidth. Within the existing R-$\lambda$model-based rate control scheme, the coding parameters ($\alpha$and$\beta$) are directly involved in the calculation of$\lambda$, and their values have a significant impact on the calculation results. By selecting precise and appropriate coding parameters, enhanced rate control and rate-distortion performance can be realized. However, during the mapping of target bits to$\lambda$,$\alpha$and$\beta$often inadequately consider the rate-distortion attributes and the content characteristics of the coding units. This paper introduces a parameter optimization algorithm for rate control in Versatile Video Coding (VVC), aimed at enhancing coding efficiency. Utilizing the actual coded contexts of the coded Coding Tree Unit (CTU) alongside pre-coding information, we establish a parameters relationship model to deliver better coding parameters according to the rate-distortion attributes of the current CTU. Furthermore, leveraging coded contexts from spatially adjacent CTUs and the feature complexity, we propose a spatial coupling strategy to further improve the preceding coding parameters, considering the content characteristics of CTU. The proposed coding parameter optimization algorithm is implemented in the rate control of the VVC test model (VTM). Experimental findings indicate that this optimization algorithm accomplishes BD-rate savings concerning Peak Signal-to-Noise Ratio (PSNR) as well as the Multiscale Structural Similarity Index Metric (MS-SSIM) across various configurations. In addition, a more stable buffer status and enhanced visual quality are visible, which highlights the benefits of the proposed algorithm.

With the global temperature rising, fires are becoming profound impact on Earth system. Accurate fire type identification based on remote sensing technology is becoming great significance for disaster prevention and reduction. In this study, we built a system framework to quickly detect fire type and analyse spatio-temporal distribution online. First, National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer (VIIRS) Active Fire (ACF), Landsat 8 fire product, Moderate Resolution Imaging Spectroradiometer (MODIS) fire product (MCD14ML), and NPP-VIIRS Nighttime Fire Data (VNF) were obtained and stored into database. Then, industrial and biological fire were distinguished by taking full advantage of the industrial heat source dataset and land use dataset. Finally, quickly analysis can be realized online directly. The result shows that that system can carry out long time series analysis of multi-source fire data on different regions directly.

Single-image super-resolution (SISR) has experienced vigorous growth with the rapid development of deep learning. However, handling arbitrary scales (e.g.,integers, non-integers, or asymmetric) using a single model remains a challenging task. Existing super-resolution (SR) networks commonly employ static convolutions during feature extraction, which cannot effectively perceive changes in scales. Moreover, these continuous-scale upsampling modules only utilize the scale factors, without considering the diversity of local features. To activate more information for better reconstruction, two plug-in and compatible modules for fixed-scale networks are designed to perform arbitrary-scale SR tasks. Firstly, we design a Scale-aware Local Feature Adaptation Module (SLFAM), which adaptively adjusts the attention weights of dynamic filters based on the local features and scales. It enables the network to possess stronger representation capabilities. Then we propose a Local Feature Adaptation Upsampling Module (LFAUM), which combines scales and local features to perform arbitrary-scale reconstruction. It allows the upsampling to adapt to local structures. Besides, deformable convolution is utilized letting more information to be activated in the reconstruction, enabling the network to better adapt to the texture features. Extensive experiments on various benchmark datasets demonstrate that integrating the proposed modules into a fixed-scale SR network enables it to achieve satisfactory results with non-integer or asymmetric scales while maintaining advanced performance with integer scales.

The amount of multimedia data, such as images and videos, has been increasing rapidly with the development of various imaging devices and the Internet, bringing more stress and challenges to information storage and transmission. The redundancy in images can be reduced to decrease data size via lossy compression, such as the most widely used standard Joint Photographic Experts Group (JPEG). However, the decompressed images generally suffer from various artifacts (e.g., blocking, banding, ringing, and blurring) due to the loss of information, especially at high compression ratios. This article presents a feature-enriched deep convolutional neural network for compression artifacts reduction (FeCarNet, for short). Taking the dense network as the backbone, FeCarNet enriches features to gain valuable information via introducing multi-scale dilated convolutions, along with the efficient 1 ×1 convolution for lowering both parameter complexity and computation cost. Meanwhile, to make full use of different levels of features in FeCarNet, a fusion block that consists of attention-based channel recalibration and dimension reduction is developed for local and global feature fusion. Furthermore, short and long residual connections both in the feature and pixel domains are combined to build a multi-level residual structure, thereby benefiting the network training and performance. In addition, aiming at reducing computation complexity further, pixel-shuffle-based image downsampling and upsampling layers are, respectively, arranged at the head and tail of the FeCarNet, which also enlarges the receptive field of the whole network. Experimental results show the superiority of FeCarNet over state-of-the-art compression artifacts reduction approaches in terms of both restoration capacity and model complexity. The applications of FeCarNet on several computer vision tasks, including image deblurring, edge detection, image segmentation, and object detection, demonstrate the effectiveness of FeCarNet further.

Good-quality video coding for low-bitrate applications is essential for narrow bandwidth transmission and limited capacity storage. In this paper, we propose an adaptive downsampling-based coding model to improve the low-bitrate compression efficiency of high-efficiency video coding (HEVC). At the encoder, the video sequence is adaptively divided into key frames (KFs) and nonkey frames (NKFs), which are encoded at the original resolution and at a reduced resolution, respectively. At the decoder, a super-resolution method based on deep learning and gradient transformation is used to upscale the NKFs. To improve the quality of NKFs without additional information during decoding, we use motion estimation to find the most similar blocks between the upscaled NKFs and the associated high-resolution KFs. Then, an adaptive patching-based method is used to warp the low-quality NKF blocks with the high-quality KF blocks. Experimental results indicate that for standard high-definition test video sequences, the maximum improvement in the peak signal-to-noise ratio can reach 3.54 dB, and the critical bitrate can reach 9.89 Mb/s at a low bitrate when compared to HEVC. These results demonstrate significant improvements compared to existing methods.

Superresolution (SR) of compressed images is chall-enging due to the combination of resolution loss and compression artifacts. To solve these intertwined problems, the conventional cascading framework splits the solution into independent deblocking and SR subprocesses, where some existing high-frequency (HF) components are often oversmoothed during deblocking and information exchange between cascaded deblocking and SR remains untouched. In this paper, we propose an iterative cascading framework after analyzing the correlation between the two subprocesses. Deblocking is provided with a shape-adaptive low-rank prior to well preserve edges and an extra prior to restore the lost HF components. The latter prior represents an important feedback link from SR to deblocking, which is a novel design in this framework. To provide an accurate and noise-robust feedback of the extra prior, an SR method via singular value decomposition projection is also developed. The extensive experimental results demonstrate the superior performance of the proposed method.

Most of the recent leading multiple magnetic resonance imaging (MRI) super‐resolution techniques for brain are limited to rigid motion. In this study, the authors aim to develop a super‐resolution technique with diffeomorphism mainly for longitudinal brain MRI data. For the images from different time slots, unpredicted deformation may occur. In previous studies, sole rigid registration or traditional non‐rigid registration has been frequently used to achieve multi‐plane super‐resolution. However, non‐rigid motion of two brains from different time slots is difficult to model, since brain contains a wealth of complex structure such as the cerebral cortex. In order to address such problem, rigid and large diffeomorphic registration has been embedded into their super‐resolution framework. In addition, many previous researchers use norm to achieve super‐resolution framework. In this work, norm minimisation and regularisation based on a bilateral prior are adopted. These operations ensure its robustness to the assumed model of data and noise. Their approach is evaluated using Alzheimer datasets from seven different resolutions. Results show that their reconstructions have advantages over rigid and conventional non‐rigid registration‐based super‐resolution, in terms of the root‐mean‐square error and structure similarity. Furthermore, their reconstruction results improve the precision of brain automatic segmentation.

Single image super-resolution (SISR) is a challenging work, which aims to recover the missing information in an observed low-resolution (LR) image and generate the corresponding high-resolution (HR) version. As the SISR problem is severely ill-conditioned, effective prior knowledge of HR images is necessary to well pose the HR estimation. In this paper, an effective SISR method is proposed via the local structure-adaptive transform-based nonlocal self-similarity modeling and learning-based gradient regularization (LSNSGR). The LSNSGR exploits both the natural and learned priors of HR images, thus integrating the merits of conventional reconstruction-based and learning-based SISR algorithms. More specifically, on the one hand, we characterize nonlocal self-similarity prior (natural prior) in transform domain by using the designed local structure-adaptive transform; on the other hand, the gradient prior (learned prior) is learned via the jointly optimized regression model. The former prior is effective in suppressing visual artifacts, while the latter performs well in recovering sharp edges and fine structures. By incorporating the two complementary priors into the maximum a posteriori-based reconstruction framework, we optimize a hybrid L1- and L2-regularized minimization problem to achieve an estimation of the desired HR image. Extensive experimental results suggest that the proposed LSNSGR produces better HR estimations than many state-of-the-art works in terms of both perceptual and quantitative evaluations.

Super-resolution (SR) from a single image plays an important role in many computer vision applications. It aims to estimate a high-resolution (HR) image from an input low- resolution (LR) image. To ensure a reliable and robust estimation of the HR image, we propose a novel single image SR method that exploits both the local geometric duality (GD) and the non-local similarity of images. The main principle is to formulate these two typically existing features of images as effective priors to constrain the super-resolved results. In consideration of this principle, the robust soft-decision interpolation method is generalized as an outstanding adaptive GD (AGD)-based local prior. To adaptively design weights for the AGD prior, a local non-smoothness detection method and a directional standard-deviation-based weights selection method are proposed. After that, the AGD prior is combined with a variational-framework-based non-local prior. Furthermore, the proposed algorithm is speeded up by a fast GD matrices construction method, which primarily relies on the selective pixel processing. The extensive experimental results verify the effectiveness of the proposed method compared with several state-of-the-art SR algorithms.

Firefly algorithm (FA) is a new meta‐heuristic optimisation algorithm that mimics the social behaviour of fireflies flying in the tropical and temperate summer sky. In this study, a novel application of FA is presented as it is applied to solve tracking problem. A general optimisation‐based tracking architecture is proposed and the parameters’ sensitivity and adjustment of the FA in tracking system are studied. Experimental results show that the FA‐based tracker can robustly track an arbitrary target in various challenging conditions. The authors compare the speed and accuracy of the FA with three typical tracking algorithms including the particle filter, meanshift and particle swarm optimisation. Comparative results show that the FA‐based tracker outperforms the other three trackers.