This paper proposes a novel chaotic block image encryption algorithm based on deep convolutional generative adversarial networks (DCGANs), quaternions, an improved Feistel network, and an overall scrambling and diffusion mechanism. First, a new hyperchaotic system is introduced and combined with DCGANs to generate a random sequence with better randomness and complexity as a key stream. This sequence is then combined with a quaternion and an improved Feistel network encryption of a colour plaintext image by utilizing the key block matrix to ultimately achieve overall scrambling and diffusion of the cipher image. Finally, the security of this algorithm is quantitatively and qualitatively analysed. The simulation results show that the proposed hyperchaotic system has a large key space and good random characteristics and that the new algorithm yields adequate security and can resist brute-force attacks and chosen-plaintext attacks. Therefore, this approach provides a new way to achieve secure transmission and protection of image information.

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A Novel Chaotic Block Image Encryption Algorithm Based on Deep Convolutional Generative Adversarial Networks

From the perspective of optimizing the visual experience of the omnidirectional system, this article explores the terminal image quality of the high dynamic range omnidirectional image (HOI) system for the first time. The HOI system has higher contrast and wider field of view perception, but its complex imaging process poses a challenge to quality measurement. In this article, we propose a novel quality measurement method for HOI (QM-HOI) system based on distortion perception and brain information-processing mechanism. Because HOI system may encounter single distortion or mixed distortion, the proposed method consists of two modules, i.e., local progressive distortion model (LPDM) and global mixed distortion model (GMDM). LPDM is designed to mainly extract local features based on the different perception effects that tone mapping (TM) operators impart to coding distortion in different luminance regions. GMDM is designed to jointly consider the structure distortion caused by coding and the color distortion of TM. The extracted features are formed into feature vectors to predict the visual quality of the distorted HOIs. A new HOI subjective evaluation dataset with multidistortion is established, called NBU-HOID, specialized for the design and testing of the proposed method. Experimental results show that the objective measurement results of the proposed method is better than the state-of-the-art methods, and more in line with the human perception. We will also provide the NBU-HOID dataset free of charge shortly afterward.

Quality Measurement for High Dynamic Range Omnidirectional Image Systems

No-reference quality index of tone-mapped images based on authenticity, preservation, and scene expressiveness

Tone-mapping technology aims to transform high dynamic range (HDR) images into standard dynamic range images for visualizing on standard screens. However, various visual artifacts will inevitably be induced during the tone-mapping process. To quantify the visual quality degradation for tone-mapped images (TMIs) accurately and automatically, a robust blind TMI quality measurement method is developed by the analysis of low-level and high-level perceptual characteristics. Specifically, for low-level visual features, considering that the tone-mapping operator is prone to destroy the local image contrast, which conveys vital structural information, we resort to the joint statistical features based on gradient map and Laplacian of Gaussian response to portray the local image contrast variation in TMIs. In addition, chromatic local binary pattern (CLBP) is leveraged to measure the colorfulness degradation over four chromatic descriptor maps. For high-level quality-aware features, deep-learned features are utilized by a pretrained convolutional neural network to characterize the semantics variation. Finally, the extracted low-level and high-level features are combined and mapped into an overall quality score by regression function. Extensive experiments show that our metric achieves a higher performance over two publicly available TMI benchmark databases.

Blind Tone-Mapped HDR Image Quality Measurement by Analysis of Low-Level and High-Level Perceptual Characteristics

Motivated by the problems of nonuniversality and over-reliance on the original reference image in High dynamic range (HDR) Image quality assessment (IQA), a convolutional neural network-based algorithm for no-reference HDR image quality assessment is proposed. The Salience detection by self-resemblance (SDSR) algorithm which extracts the salient regions of the HDR image, is used to simulate the human visual attention mechanism. Then a visual quality perception network for training quality prediction models is designed according to the visual characteristics of luminance and contrast sensitivity. And this network consists of an Error estimation network (Error-net), a Perceptual resistance network (PR-net) and a mixing function. The experimental results indicate that the method proposed has high consistency with subjective perception, and the value of assessment metrics Spearman rank-order correlation coefficient (SROCC), Pearson product-moment correlation coefficient (PLCC) and Root mean square error (RMSE) correspondingly reaches 0.941, 0.910 and 8.176 as well. It is comparable with classic full-reference HDR IQA methods.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/cje.2021.01.008

CNN Based No-Reference HDR Image Quality Assessment

Tone mapping operators (TMOs) reproduce the high dynamic range (HDR) images on low dynamic range (LDR) consumer electronics devices such as monitors or printers. To accurately measure and compare the performance of different TMOs, this article proposes a tone-mapped images (TMIs) luminance partition model and corresponding quality measure. First, each tone-mapped (TM) image is segmented into highlight region (HR), dark region (DR) and midtone region (MR) based on luminance partition. Second, local entropies and contrast features are extracted in the HR and DR, and color-based features are captured in the MR. Meanwhile, the gray-level co-occurrence matrix (GLCM) and Canny operator are utilized to measure the microstructural distortions and halo effects, respectively. Finally, all extracted features are combined and trained together with subjective ratings to form a regression model using support vector regression (SVR). Experimental results show that the proposed method outperforms the state-of-the-art no-reference (NR) methods. Specifically, the spearman correlation coefficients (SRCC) values of our method reach 0.83 and 0.76 on the tone-mapped image database (TMID) and the ESPL-LIVE HDR database, respectively.

Tone-Mapped Image Quality Assessment for Electronics Displays by Combining Luminance Partition and Colorfulness Index

Research on tone mapping operators (TMOs) attracts more attention recently, which can transform high dynamic range (HDR) images to low dynamic range (LDR) images for visualizing them on the common displays. In this paper, we propose a novel no-reference image quality assessment (IQA) model to evaluate the perceptual quality of tone-mapped images (TMIs). Specifically, local phase congruency (LPC) is first computed to evaluate the image sharpness and some statistical characteristics are extracted on the edge maps to measure the halo effect. Meanwhile, TMIs are transformed to opponent color (OC) space to gain the global image chro-maticity and local image contract in the chromatic field. Finally, a regression module is learnt using support vector regression (SVR) to train the mapping function that maps all the features to subjective quality scores. The model shows admirable performance when tested on ESPL-LIVE HDR image database.

A Novel No-Reference Quality Assessment Model of Tone-Mapped HDR Image

The invention provides a non-reference tone mapping image quality evaluation method based on multi-feature fusion. The method mainly comprises a feature extraction stage and a training regression stage. In the feature extraction stage, features of an image are extracted in three fields, and entropy features of the image, texture features based on a gray level co-occurrence matrix and natural scenestatistical features are extracted in the pixel field. In the field of ambiguity, local phase consistency is adopted to evaluate the overall ambiguity, and small block features are extracted from anedge region to measure the halo effect. In the color field, an image is converted into an opposite color space, and overall color information and contrast information of all channels are measured. Finally, the quality of the tone mapping image is predicted by using a machine learning method. The algorithm provided by the invention can accurately and effectively predict the quality of the tone mapping image.

Min Zhao

Papers

Tone-Mapped Image Quality Assessment for Electronics Displays by Combining Luminance Partition and Colorfulness Index

A Novel No-Reference Quality Assessment Model of Tone-Mapped HDR Image

No-reference tone mapping image quality evaluation method based on multi-feature fusion