Wireless Sensor Networks (WSN) is arising as a potential computing platform in diverse zones such as weather forecasting, modern robotization, medical health care, and military systems, etc. Since the sensors are constantly gathering information from the actual world and communicate with one another through remote connections, keeping up the security and protection of WSN communication is a prerequisite. In this paper, safe confirmation and key organization scheme dependent on Elliptic Curve Cryptography (ECC) has been suggested to make sure about information/picture transmission in WSNs. The scheme proposed in this paper is protected, competent, and appropriate for providing sensor technology based IoT services and applications. The protocol provides all the security features such as mutual authentication, confidentiality, data integrity, perfect forward secrecy, fair key agreement, etc. and is secure against hello flood attack, DoS attack, man-in-middle attack, etc. Simulation software AVISPA has confirmed the safety of the protocol for the known assaults. The performance analysis ensures the superiority of the projected proposal over the existing schemes. Index Terms – WSN, Security, Elliptical Curve Cryptography (ECC), Automated Validation of Internet Security Protocols and Applications (AVISPA).

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Elliptic Curve Cryptography based Secure Image Transmission in Clustered Wireless Sensor Networks

The 4K content can deliver a more immersive visual experience to consumers due to the huge improvement in spatial resolution. However, the high spatial resolution brings a great challenge for video transmission and storage. Therefore, it is necessary to compress or downscale the 4K content before transmitting it to end-users. Existing blind image quality assessment (BIQA) methods are not suitable for 4K contents due to the high spatial resolution and specific distortions caused by upscaling methods. In this paper, we propose a deep learning-based BIQA model for 4K content, aiming to recognize true and pseudo 4K content and meanwhile evaluate their visual quality. Considering the characteristic that high spatial resolution can represent more abundant high-frequency information, we first propose a Grey-level Co-occurrence Matrix (GLCM) based texture complexity measure to select three representative image patches from a 4K image, which can reduce the computational complexity and is proven to be very effective for the overall quality prediction. Then, we extract various visual features from the intermediate layers of the convolutional neural network (CNN) and integrate them into the quality-aware feature representation. Finally, two multilayer perception (MLP) networks are utilized to map the quality-aware features into the class probability and the quality score of each patch respectively. The overall quality index is obtained through averaging the results of all patches. The proposed model is trained via the multi-task learning manner and the uncertainty principle is introduced to balance the losses of the classification and regression tasks. The experimental results show that the proposed model outperforms all compared BIQA metrics on four 4K content quality assessment databases.

Deep Neural Network for Blind Visual Quality Assessment of 4K Content

With the development of rendering techniques, computer graphics generated images (CGIs) have been widely used in practical application scenarios such as architecture design, video games, simulators, movies, etc. Different from natural scene images (NSIs), the distortions of CGIs are usually caused by poor rending settings and limited computation resources. What's more, some CGIs may also suffer from compression distortions in transmission systems like cloud gaming and stream media. However, limited work has been put forward to tackle the problem of computer graphics generated images' quality assessment (CG-IQA). Therefore, in this paper, we establish a large-scale subjective CG-IQA database to deal with the challenge of CG-IQA tasks. We collect 25,454 in-the-wild CGIs through previous databases and personal collection. After data cleaning, we carefully select 1,200 CGIs to conduct the subjective experiment. Several popular no-reference image quality assessment (NR-IQA) methods are tested on our database. The experimental results show that the handcrafted-based methods achieve low correlation with subjective judgment and deep learning-based methods obtain relatively better performance. The current NR-IQA models are not suitable for CG-IQA tasks and more effective models are urgently needed.

Subjective Quality Assessment for Images Generated by Computer Graphics

In a new era for machine vision, some image enhancement algorithms can be used to improve the quality of an imageset without reference. To assess the performance of imageset enhancement, the existing average criterion utilizes a no-reference image quality metric to calculate the quality score of each enhanced image, and quantifies the performance of an enhancement algorithm by the mean value of all scores. If the quality scores of some images fluctuate greatly, their mean value difficultly reflects the degradation or worst cases during imageset enhancement. Therefore, this paper analyzes and illustrates the need and significance of consistency enhancement assessment, and then proposes a subset-guided consistency enhancement assessment criterion for an imageset without reference. By measuring the subset of an imageset, the proposed criterion firstly calculates the difference of quality scores of each image before and after enhancement and then filters the outlier data outside confidence interval, and finally quantifies the consistency enhancement performance of an enhancement algorithm according to its consistency enhancement degree. When a small subset is used to guide its large imageset, the average criterion judges a consistency or non-consistency enhancement algorithm with a 16.7% false identification ratio, and also makes one misjudgment about the optimal-consistency algorithm, while the proposed criterion always correctly judges the non-consistency or optimal-consistency enhancement algorithm. This paper can help the scientific community to select a robust enhancement algorithm in the degradation or worst cases. As compared with the average criterion, the proposed criterion is more robust in terms of subset-guided consistency enhancement assessment, which may effectively find an optimal-consistency or non-consistency enhancement algorithm for the rest of an imageset.

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Subset-Guided Consistency Enhancement Assessment Criterion for an Imageset Without Reference

Lossy compression algorithms trade bits for quality, aiming at reducing as much as possible the bitrate needed to represent the original source (or set of sources), while preserving the source quality. In this letter, we propose a novel paradigm of compression algorithms, aimed at minimizing the information loss perceived by the final user instead of the actual source quality loss, under compression rate constraints. As main contributions, we first introduce the concept of perceived information (PI), which reflects the information perceived by a given user experiencing a data collection, and which is evaluated as the volume spanned by the sources features in a personalized latent space. We then formalize the rate-PI optimization problem and propose an algorithm to solve this compression problem. Finally, we validate our algorithm against benchmark solutions with simulation results, showing the gain in taking into account users’ preferences while also maximizing the perceived information in the feature domain.

/pdf/large-database-compression-based-on-perceived-information-1gcj9y9cv0.pdf

Large Database Compression Based on Perceived Information

Image set compression has recently become an active research topic due to the explosion of digital photographs. In order to efficiently compress the image sets of similar images with moving objects, in this paper, we propose a novel algorithm for image set compression using multiple reference images. First, for an image set, its depth-constrained minimum arborescence is generated. We then present a reference image candidate determination method to build the reference image candidates for the images of the set. Furthermore, we propose a rate-distortion optimized multiple reference image selection method. This method compares the correlation between every image and each of its reference image candidates to produce its multiple reference images. Finally, compressed image data are achieved by employing block-based motion compensation and residue coding. In addition, we also give a new way of access to images to keep the same access delay with single reference image-based schemes. Compared with the state-of-the-art image compression algorithms, experimental results show that our proposed algorithm can significantly improve the image compression performance.

Novel Image Set Compression Algorithm Using Rate-Distortion Optimized Multiple Reference Image Selection

Perceptual Quality Assessment for Fine-Grained Compressed Images

This Letter first proposes an algorithm for compressing the sets of the similar images with priorities. The proposed algorithm includes four modules, i.e. priority assigned, depth- and priority-constrained minimum arborescence generation, disparity reduction, and residue coding. Experimental results demonstrate that the proposed algorithm can effectively compress the similar images with priorities, and achieves similar rate-distortion performance to the state-of-the-art image set compression algorithm which has no constraints of depth and priority.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/el.2018.7342

Image set compression for similar images with priorities

Image deletion and image insertion, the current commonly used two kinds of image set management, refer to removing compressed images from and adding new photos to a compressed image set, respectively. However, they do not deal with well the problem of the combination of images selected from multiple compressed image sets. To address this issue, in this paper we first propose an image subset union algorithm for compressed image sets. Image subset union aims to integrate image subsets derived from multiple compressed image sets into a new compressed image set. First, we put forward a way to identify the root vertex candidate of the new compressed image set. Second, we classify all the images of image subsets into three categories: images needed to be re-encoded, images needed to be only decoded, and images unneeded to be re-encoded or decoded. Third, we also employ minimum spanning tree production, a proposed vertex layer candidate assignment method, depth- and subtree-constrained minimum spanning tree generation, as well as image re-encoding to build the new compressed image set. Experimental results show that our proposed algorithm can effectively accomplish image subset union with low complexity. 

Wei Wu

Papers

Novel Image Set Compression Algorithm Using Rate-Distortion Optimized Multiple Reference Image Selection

Perceptual Quality Assessment for Fine-Grained Compressed Images

Image set compression for similar images with priorities

Image Subset Union for Compressed Image Sets in Cloud Servers