Optimizing Image Compression via Joint Learning with Denoising

High levels of noise usually exist in today's captured images due to the relatively small sensors equipped in the smartphone cameras, where the noise brings extra challenges to lossy image compression algorithms. Without the capacity to tell the difference between image details and noise, general image compression methods allocate additional bits to explicitly store the undesired image noise during compression and restore the unpleasant noisy image during decompression. Based on the observations, we optimize the image compression algorithm to be noise-aware as joint denoising and compression to resolve the bits misallocation problem. The key is to transform the original noisy images to noise-free bits by eliminating the undesired noise during compression, where the bits are later decompressed as clean images. Specifically, we propose a novel two-branch, weight-sharing architecture with plug-in feature denoisers to allow a simple and effective realization of the goal with little computational cost. Experimental results show that our method gains a significant improvement over the existing baseline methods on both the synthetic and real-world datasets. Our source code is available at https://github.com/felixcheng97/DenoiseCompression.Comment: Accepted to ECCV 202

HyperThumbnail: Real-time 6K Image Rescaling with Rate-distortion Optimization

Contemporary image rescaling aims at embedding a high-resolution (HR) image into a low-resolution (LR) thumbnail image that contains embedded information for HR image reconstruction. Unlike traditional image super-resolution, this enables high-fidelity HR image restoration faithful to the original one, given the embedded information in the LR thumbnail. However, state-of-the-art image rescaling methods do not optimize the LR image file size for efficient sharing and fall short of real-time performance for ultra-high-resolution (e.g., 6K) image reconstruction. To address these two challenges, we propose a novel framework (HyperThumbnail) for real-time 6K rate-distortion-aware image rescaling. Our framework first embeds an HR image into a JPEG LR thumbnail by an encoder with our proposed quantization prediction module, which minimizes the file size of the embedding LR JPEG thumbnail while maximizing HR reconstruction quality. Then, an efficient frequency-aware decoder reconstructs a high-fidelity HR image from the LR one in real time. Extensive experiments demonstrate that our framework outperforms previous image rescaling baselines in rate-distortion performance and can perform 6K image reconstruction in real time.Comment: Accepted by CVPR 2023; Github Repository: https://github.com/AbnerVictor/HyperThumbnai

Modelling of coal trade process for the logistics enterprise and its optimisation with stochastic predictive control

In the paper, a typical coal trade process is described and modelled, where one logistics enterprise with blending equipments lies in the core and two types of common contracts are elucidated to define constraints. A mixed-integer model is built and featured by addressing contract violation, blending operation, real-time price information and arbitrarily distributed stochastic demands. To deal with the stochastic demands, probabilistic constraints are formed. Accordingly, stochastic model predictive control strategy with both receding horizon and decreasing horizon formulations is developed to handle the probabilistic constraints and exploit the value of newest price information. By solving a series of mixed-integer linear programmes, optimal coal trade decisions for the logistics enterprise can be obtained, including procurement decision, selling decision and operational decision of the blending equipments. Thorough simulation experiments are carried out and compared with three different strategies, which interpret the effectiveness of the proposed strategy.In part by the National Natural Science Foundation of China [61304090] and the Department of Education of Liaoning Province, China [L2013132].http://www.tandfonline.com/loi/tprs202016-07-30hb201

AUTOMATED ADVERTISEMENT CREATION SYSTEM

An advertisement creation system generates sizeless creatives and renders the creatives as display advertisements of any arbitrary dimension. The system extracts text assets and image assets from creatives provided by an advertiser. In particular, the system selects assets to display based on scores for the respective text assets and image assets. The system then combines selected text assets and image assets to generate a final creative. Finally, the system optimizes the final creative and renders the final creative for display

The Logistic Regression from the Viewpoint of the Factor Space Theory

Logistic regression plays an important role in machine learning. People excitingly use it in conceptual matching yet with some details to be understood further. This paper aims to present a reasonable statement on logistic regression based on fuzzy sets and the factor space theory. An example about breast cancer diagnosis is displayed to show how the factor space theory can be incorporated into the understanding and use of logistic regression

Recent Advances and New Perspectives in Surgery of Renal Cell Carcinoma

Renal cell carcinoma (RCC) is one of the most common types of cancer in the urogenital system. For localized renal cell carcinoma, nephron-sparing surgery (NSS) is becoming the optimal choice because of its advantage in preserving renal function. Traditionally, partial nephrectomy is performed with renal pedicle clamping to decrease blood loss. Furthermore, both renal pedicle clamping and the subsequent warm renal ischemia time affect renal function and increase the risk of postoperative renal failure. More recently, there has also been increasing interest in creating surgical methods to meet the requirements of nephron preservation and shorten the renal warm ischemia time including assisted or unassisted zero-ischemia surgery. As artificial intelligence increasingly integrates with surgery, the three-dimensional visualization technology of renal vasculature is applied in the NSS to guide surgeons. In addition, the renal carcinoma complexity scoring system is also constantly updated to guide clinicians in the selection of appropriate treatments for patients individually. In this article, we provide an overview of recent advances and new perspectives in NSS

Dorsal Visual Pathway Changes in Patients with Comitant Extropia

BACKGROUND: Strabismus is a disorder in which the eyes are misaligned. Persistent strabismus can lead to stereopsis impairment. The effect of strabismus on human brain is not unclear. The present study is to investigate whether the brain white structures of comitant exotropia patients are impaired using combined T1-weighted imaging and diffusion tensor imaging (DTI). PRINCIPAL FINDINGS: Thirteen patients with comitant strabismus and twelve controls underwent magnetic resonance imaging (MRI) with acquisition of T1-weighted and diffusion tensor images. T1-weighted images were used to analyze the change in volume of white matter using optimized voxel-based morphology (VBM) and diffusion tensor images were used to detect the change in white matter fibers using voxel-based analysis of DTI in comitant extropia patients. VBM analysis showed that in adult strabismus, white matter volumes were smaller in the right middle occipital gyrus, right occipital lobe/cuneus, right supramarginal gyrus, right cingulate gyrus, right frontal lobe/sub-gyral, right inferior temporal gyrus, left parahippocampa gyrus, left cingulate gyrus, left occipital lobe/cuneus, left middle frontal gyrus, left inferior parietal lobule, and left postcentral gyrus, while no brain region with greater white matter volume was found. Voxel-based analysis of DTI showed lower fractional anisotropy (FA) values in the right middle occipital gyrus and right supramarginal gyrus in strabismus patients, while brain region with increased FA value was found in the right inferior frontal gyrus. CONCLUSION: By combining VBM and voxel-based analysis of DTI results, the study suggests that the dorsal visual pathway was abnormal or impaired in patients with comitant exotropia

Robust & stochastic model predictive control

In the thesis, two different model predictive control (MPC) strategies are investigated for linear systems with uncertainty in the presence of constraints: namely robust MPC and stochastic MPC. Firstly, a Youla Parameter is integrated into an efficient robust MPC algorithm. It is demonstrated that even in the constrained cases, the use of the Youla Parameter can desensitize the costs to the effect of uncertainty while not affecting the nominal performance, and hence it strengthens the robustness of the MPC strategy. Since the controller u = K x + c can offer many advantages and is used across the thesis, the work provides two solutions to the problem when the unconstrained nominal LQ-optimal feedback K cannot stabilise the whole class of system models.The work develops two stochastic tube approaches to account for probabilistic constraints. By using a semi closed-loop paradigm, the nominal and the error dynamics are analyzed separately, and this makes it possible to compute the tube scalings offline. First, ellipsoidal tubes are considered. The evolution for the tube scalings is simplified to be affine and using Markov Chain model, the probabilistic tube scalings can be calculated to tighten the constraints on the nominal. The online algorithm can be formulated into a quadratic programming (QP) problem and the MPC strategy is closed-loop stable. Following that, a direct way to compute the tube scalings is studied. It makes use of the information on the distribution of the uncertainty explicitly. The tubes do not take a particular shape but are defined implicitly by tightened constraints. This stochastic MPC strategy leads to a non-conservative performance in the sense that the probability of constraint violation can be as large as is allowed. It also ensures the recursive feasibility and closed-loop stability, and is extended to the output feedback case.</p