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Prevalent video codec implementations deployed in the field often solely support chrominance sub-sampled video data represented by the YCbCr 4:2:0 format. For certain applications like screen sharing, however, chroma sub-sampling leads to disturbing artifacts, especially for text or graphics with thin lines. It is desirable to reduce these artifacts while maintaining compatibility with all user devices. For this reason, an enhancement layer coding framework for chroma-format scalable coding with specific focus on screen content is proposed in this paper. Based on an analysis of screen content data characteristics, the enhancement layer codec is optimized specifically for this content class, is of low algorithmic complexity, and applicable with any image or video codec for base layer compression in a joint coding system. The system is intentionally not designed as a general purpose lossless YCbCr 4:4:4 coding scheme, but instead chooses to close a quality gap that prevalent video codecs do not address. Experimental analysis reveals an average BD-PSNR gain in the RGB domain of 1.0 dB comparing the proposed two-layer scalable coding approach to single layer compression using base layer coding only. In relation to simulcast coding, an average BDR RGB difference of 8.1% is observed.

Considering multidimensional structure of the multi-exposure images,a new Tensor product and Tensorsingular value decomposition based Multi-Exposure image Fusion (TT-MEF) method is proposed. The main innovation of this work is to explore a new feature representation of multi-exposure images in the new tensor domain and design the fusion strategy on this basis. Specifically,the luminance and the chrominance channels are fused separately to maintain color consistency. For the luminance fusion,the luminance channel of multi-exposure images is divided into two parts,that is,de-mean term and mean term. The de-mean term is represented as a tensor to extract the feature. Then,the tensor product and tensor-singular value decomposition (T-SVD) are used to design a tensor feature extractor. Furthermore,a fusion strategy of the de-mean term is presented according to the visual saliency model,and a fusion strategy of the mean term is defined by the local and the global visual weights to control counterpoise between the local and global luminance. For the chrominance fusion,a new fusion strategy is also designed by the tensor product and T-SVD,similar to the luminance fusion. Finally,the fused image is obtained by combining the luminance and chrominance fusion. Experimental results show that the proposed TT-MEF method generally outperforms the existing state-of-the-art in terms of subjective visual quality and objective evaluation.

The huge amount of complex scene information recorded by light field imaging has the prospect of immersive media applications. Compression and reconstruction algorithms are crucial for the transmission, storage, and display of such massive data. Most of the existing quality evaluation indexes do not take an effective account of light field characteristics. To accurately evaluate the distortions caused by compression and reconstruction algorithms, it is necessary to construct an image evaluation index that reflects the angular-spatial characteristic of the light field. This work proposes a full reference light field image quality evaluation index, which attempts to extract less information from the focus stack to accurately evaluate the entire light field quality. The proposed framework includes three specific steps. Firstly, we construct a key refocused images extraction framework by the maximal spatial information contrast and the minimal angular information variation. Specifically, the gradient and phase congruency operators are used in the extraction framework. Secondly, a novel light field quality evaluation index is built based on the angular-spatial characteristic of the key refocused images. In detail, the features used in the key refocused images extraction framework and the chrominance feature are combined to construct the union feature. Then the similarity of the union feature is pooled by the relevant visual saliency map to get the predicted score. Finally, the overall quality of the light field is measured by applying the proposed index to the key refocused images. The high efficiency and precision of the proposed method are shown by extensive comparison experiments.

Existing image fusion methods always use the same representations for different modal medical images. Otherwise, they solve the fusion problem by subjectively defining characteristics to be preserved. However, it leads to the distortion of unique information and restricts the fusion performance. To address the limitations, this paper proposes an unsupervised enhanced medical image fusion network. We perform both surface-level and deep-level constraints for enhanced information preservation. The surface-level constraint is based on the saliency and abundance measurement to preserve the subjectively defined and intuitive characteristics. In the deep-level constraint, the unique information is objectively defined based on the unique channels of a pre-trained encoder. Moreover, in our method, the chrominance information of fusion results is also enhanced. It is because we use the high-quality details in structural images (e.g., MRI) to alleviate the mosaic in functional images (e.g., PET, SPECT). Both qualitative and quantitative experiments demonstrate the superiority of our method over the state-of-the-art fusion methods.

With the improvement of sensor technology and significant algorithmic advances, the accuracy of remote heart rate monitoring technology has been significantly improved. Despite of the significant algorithmic advances, the performance of rPPG algorithm can degrade in the long-term, high-intensity continuous work occurred in evenings or insufficient light environments. One of the main challenges is that the lost facial details and low contrast cause the failure of detection and tracking. Also, insufficient lighting in video capturing hurts the quality of physiological signal. In this paper, we collect a large-scale dataset that was designed for remote heart rate estimation recorded with various illumination variations to evaluate the performance of the rPPG algorithm (Green, ICA, and POS). We also propose a low-light enhancement solution (technical solution) for remote heart rate estimation under the low-light condition. Using collected dataset, we found 1) face detection algorithm cannot detect faces in video captured in low light conditions; 2) A decrease in the amplitude of the pulsatile signal will lead to the noise signal to be in the dominant position; and 3) the chrominance-based method suffers from the limitation in the assumption about skin-tone will not hold, and Green and ICA method receive less influence than POS in dark illuminance environment. The proposed solution for rPPG process is effective to detect and improve the signal-to-noise ratio and precision of the pulsatile signal., Comment: Accepted by FG2020

Video piracy is a challenging issue in the modern world. Approximately $$90\%$$ 90 % of newly released films were illegally distributed around the world via the Internet. To overcome this issue, video watermarking is an effective process that integrates a logo in video frames as a watermark. Therefore, this paper presents an efficient lossless video-watermarking scheme based on optimal keyframe selection using an intelligent gravitational search algorithm in linear wavelet transform. This technique obtains color motion and motionless frames from the cover video by the histogram difference method. One-level linear wavelet transform is performed on the chrominance channel of motion frames and a low-frequency sub-band LL opts for watermark embedding. The performance of the proposed technique has been evaluated against 12 video processing attacks in terms of imperceptibility and robustness. Experiments demonstrate that the proposed technique outperforms five state-of-the-art schemes on the considered attacks.

The agricultural production rate plays a pivotal role in the economic development of a country. However, plant diseases are the most significant impediment to the production and quality of food. The identification of plant diseases at an early stage is crucial for global health and wellbeing. The traditional diagnosis process involves visual assessment of an individual plant by a pathologist through on-site visits. However, manual examination for crop diseases is restricted because of less accuracy and the small accessibility of human resources. To tackle such issues, there is a demand to design automated approaches capable of efficiently detecting and categorizing numerous plant diseases. Precise identification and classification of plant diseases is a tedious job due because of the occurrence of low-intensity information in the image background and foreground, the huge color resemblance in the healthy and diseased plant areas, the occurrence of noise in the samples, and changes in the position, chrominance, structure, and size of plant leaves. To tackle the above-mentioned problems, we have introduced a robust plant disease classification system by introducing a Custom CenterNet framework with DenseNet-77 as a base network. The presented method follows three steps. In the first step, annotations are developed to get the region of interest. Secondly, an improved CenterNet is introduced in which DenseNet-77 is proposed for deep keypoints extraction. Finally, the one-stage detector CenterNet is used to detect and categorize several plant diseases. To conduct the performance analysis, we have used the PlantVillage Kaggle database, which is the standard dataset for plant diseases and challenges in terms of intensity variations, color changes, and differences found in the shapes and sizes of leaves. Both the qualitative and quantitative analysis confirms that the presented method is more proficient and reliable to identify and classify plant diseases than other latest approaches.

The classical chroma subsampling involves downsampling chrominance components (U and V) while maintaining the luminance component (Y). Recently, a study has attempted to change Y for the chroma-subsampling process, and improved results in the reconstructed image quality are obtained. However, computational complexity remains an issue because the study examines all the candidates in the determined range for minimal pixel distortion for the modified Y; the complexity can be high if the range is large. In this study, we reduced the candidate number to seven at most, which is mathematically optimized. Boundary points for in-the-range red, green, and blue (RGB) values are first decided, followed by the determination of the intervals that concatenate the entire curve (at most seven intervals). Each interval belongs to one of the seven sub-cases of different linear combinations of individual curves. The optimal solution (modified Y) for each sub-case is derived, which is in highly efficient form. Compared with the existing method, the proposed fast method ensures that the image quality is preserved, while the number of search candidates is reduced by 61.30%–69.19% on average, and the computational time of the process is reduced by 31.39%–58.97%, thereby demonstrating an efficient performance.

Video coding is more important than ever due to emergence of larger resolutions, new formats requiring higher bandwidths, prevalence of new video services, etc. Consequently, new codecs are designed to minimize transmission rates of such contents without sacrificing quality. This article proposes an intraprediction solution for future video codecs that save the bit rates of intramode signaling for luminance and chrominance blocks. In doing so, modes are implicitly derived at the decoder side through a gradient-based analysis. In contrast to state-of-the-art intraprediction that uses a single mode for a luma block, the proposed method offers a combination of multiple derived modes. Furthermore, the chrominance components Cb/Cr are allowed to have their separate modes, instead of sharing one mode. The implementation of the proposed method in the Versatile Video Coding reference software (VTM) provides 0.72% Bjontegaard-delta rate saving, with run-time complexity of 110% and 104% at the encoder and decoder sides, respectively.

The paper proposes a novel frame-wise filtering method based on Convolutional Neural Networks (CNNs) for enhancing the quality of HEVC decoded videos. A novel deep neural network architecture is proposed for post-filtering the entire intra-coded videos. A novel scheme utilizing frame-size patches is employed for training the network. The proposed method filters the luminance channel separately from the pair of chrominance channels. A novel patch generation paradigm is proposed where, for each color channel, the corresponding mode map is generated based on the HEVC intra-prediction mode index and block segmentation. The proposed CNN-based filtering method is an alternative to the traditional HEVC built-in in-loop filtering module for intra-coded frames. Experimental results on standard test sequences show that the proposed method outperforms the HEVC standard with average BD-rate savings of 11.1% and an average BD-PSNR improvement of 0.602 dB. The average relative improvement in $\Delta $ PSNR is around 105% at $QP = 42$ and around 85% at $QP = 32$ compared with state-of-the-art machine-learning-based methods.

In this article, we propose a quality assessment model-based on the projection invariant feature and the visual saliency for Stereoscopic Omnidirectional Images (SOIs). Firstly, the projection invariant monocular and binocular features of SOI are derived from the Scale-Invariant Feature Transform (SIFT) points to tackle the inconsistency between the stretched projection formats and the viewports. Secondly, the visual saliency model, which combines the chrominance and contrast perceptual factors, is used to facilitate the prediction accuracy. Thirdly, according to the characteristics of the panoramic image, we generate the weight map and utilize it as a location prior, which can be adapted to different projection formats. Finally, the proposed SOI quality assessment model fuses the projection invariant features, visual saliency, and location prior. Experimental results on both the NingBo University SOI Database (NBU-SOID) and Stereoscopic OmnidirectionaL Image quality assessment Database (SOLID) demonstrate the proposed metric on equi-rectangular projection format outperforms the state-of-the-art schemes, the pearson linear correlation coefficient and spearman rank order correlation coefficient performance are 0.933 and 0.933 on SOLID, and 0.907 and 0.910 on NBU-SOID, respectively. Meanwhile, the proposed algorithm is extended to another five representative projection formats and achieves superior performance.

Remote photoplethysmography (rPPG) is a non-contact technique for measuring cardiac signals from facial videos. High-quality rPPG pulse signals are urgently demanded in many fields, such as health monitoring and emotion recognition. However, most of the existing rPPG methods can only be used to get average heart rate (HR) values due to the limitation of inaccurate pulse signals. In this paper, a new framework based on generative adversarial network, called PulseGAN, is introduced to generate realistic rPPG pulse signals through denoising the chrominance signals. Considering that the cardiac signal is quasi-periodic and has apparent time-frequency characteristics, the error losses defined in time and spectrum domains are both employed with the adversarial loss to enforce the model generating accurate pulse waveforms as its reference. The proposed framework is tested on the public UBFC-RPPG database in both within-database and cross-database configurations. The results show that the PulseGAN framework can effectively improve the waveform quality, thereby enhancing the accuracy of HR, the heart rate variability (HRV) and the interbeat interval (IBI). The proposed method achieves the best performance compared to the denoising autoencoder (DAE) and CHROM, with the mean absolute error of AVNN (the average of all normal-to-normal intervals) improving 20.85% and 41.19%, and the mean absolute error of SDNN (the standard deviation of all NN intervals) improving 20.28% and 37.53%, respectively, in the cross-database test. This framework can be easily extended to other existing deep learning based rPPG methods, which is expected to expand the application scope of rPPG techniques., 10 pages, 11 figures

Traditional watermarking algorithms have been extensively studied. As an important type of watermarking schemes, template-based approaches maintain a very high embedding rate. In such scheme, the message is often represented by some dedicatedly designed templates, and then the message embedding process is carried out by additive operation with the templates and the host image. To resist potential distortions, these templates often need to contain some special statistical features so that they can be successfully recovered at the extracting side. But in existing methods, most of these features are handcrafted and too simple, thus making them not robust enough to resist serious distortions unless very strong and obvious templates are used. Inspired by the powerful feature learning capacity of deep neural network, we propose the first deep template-based watermarking algorithm in this paper. Specifically, at the embedding side, we first design two new templates for message embedding and locating, which is achieved by leveraging the special properties of human visual system, i.e. , insensitivity to specific chrominance components, the proximity principle and the oblique effect. At the extracting side, we propose a novel two-stage deep neural network, which consists of an auxiliary enhancing sub-network and a classification sub-network. Thanks to the power of deep neural networks, our method achieves both digital editing resilience and camera shooting resilience based on typical application scenarios. Through extensive experiments, we demonstrate that the proposed method can achieve much better robustness than existing methods while guaranteeing the original visual quality.

Existing techniques for hand-written digit recognition (HDR) rely heavily on the hand-coded key points and requires prior knowledge. Training an efficient HDR network with these preconditions is a complicated task. Recently, work on HDR is mainly focused on deep learning (DL) approaches and has exhibited remarkable results. However, effective detection and classification of numerals is still a challenging task due to people’s varying writing styles and the presence of blurring, distortion, light and size variations in the input sample. To cope with these limitations, we present an effective and efficient HDR system, introducing a customized faster regional convolutional neural network (Faster-RCNN). This approach comprises three main steps. Initially, we develop annotations to obtain the region of interest. Then, an improved Faster-RCNN is employed in which DenseNet-41 is introduced to compute the deep features. Finally, the regressor and classification layer is used to localize and classify the digits into ten classes. The performance of the proposed method is analyzed on the standard MNIST database, which is diverse in terms of changes in lighting conditions, chrominance, shape and size of digits, and the occurrence of blurring and noise effects, etc. Additionally, we have also evaluated our technique over a cross-dataset scenario to prove its efficacy. Experimental evaluations demonstrate that the approach is more competent and able to accurately detect and classify numerals than other recent methods.

Haze can seriously affect the visible and visual quality of outdoor optical sensor systems, e.g., driving assistance, remote sensing, and video surveillance. Single image dehazing is an intractable problem due to its ill-posed nature. The main idea of the paper is combining multi-scale fusion strategy and prior knowledge, thereby presenting balanced image contrast enhancement and intrinsic color preservation, efficiently. The atmospheric illumination prior (AIP) has been proved that haze mainly degrades the contrast of the luminance channel rather than chrominance channels in YCrCb colorspace. To this end, we firstly identify and remove the color veil (unbalanced color channel) with the white balance algorithm, to reduce the influence of unbalanced color channels neglected by the AIP. Considering the new observation that hazy regions exhibit low contrast with high-intensity pixels, the dense and mild haze are enhanced by a set of histogram modification techniques, respectively. Then, with the derived inputs, multi-scale fusion based on Laplacian decomposition strategy is proposed to blend visual contrast only in the luminance channel. Without relying on complex enhancement algorithms and only dealing with one channel, the proposed method is attractive for real-time applications. Moreover, The proposed method can be directly applied to the video sequences frame by frame, alleviating visual artifacts. The simulation results show that our method is comparative to and even better than the more complex state-of-the-art techniques.

We propose an efficient low-light image enhancement algorithm based on an optimization-based approach for gamma correction parameter estimation. We first separate an input color image into the luminance and chrominance channels, and then normalize the luminance channel using the logarithmic function to make it consistent with the human perception. Then, we divide the luminance image into dark and bright regions, and estimate the optimal gamma correction parameter for each region independently. Specifically, based on the statistical properties of the input image, we formulate a convex optimization problem that maximizes the image contrast subject to the constraint on the gamma value. By efficiently solving the optimization problems using the convex optimization theories, we obtain the optimal gamma parameter for each region. Finally, we obtain an enhanced image by merging the independently enhanced dark and bright regions with the optimal gamma parameters. Experimental results on real-world images demonstrate that the proposed algorithm can provide higher enhancement performance than state-of-the-art algorithms in terms of both subjective and objective evaluations, while providing a substantial improvement in speed.

The past years have seen a rapid development in the area of image compression techniques, mainly due to the need of fast and efficient techniques for storage and transmission of data among individuals. Compression is the process of representing the data in a compact form rather than in its original or incompact form. In this paper, integer implementation of Arithmetic Coding (AC) and Discreet Cosine Transform (DCT) were applied to colored images. The DCT was applied using the YCbCr color model. The transformed image was then quantized with the standard quantization tables for luminance and chrominance. The quantized coefficients were scanned by zigzag scan and the output was encoded using AC. The results showed a decent compression ratio with high image quality.

Avocado undergoes quality transformation during storage, which needs to be managed in order to prevent quantity losses. A machine vision system devised with a smartphone camera was used to capture ‘Hass’ avocado images. Color features in L*a*b* and YUV (YUV color space is defined in terms of one luminance (Y) and two chrominance components (U and Y)) were extracted from the RGB images. Artificial Neural Network (ANN) and Support Vector Regression (SVR) were used compared for firmness estimation using the L*a*b* and YUV color features. The results indicated the ANN model is more accurate and robust than the SVR model for estimating ‘Hass’ avocado firmness with R2, RMSE, and RPD of 0.94, 0.38, 4.03 respectively for the model testing data set. It was concluded that the machine vision system devised with a smartphone camera and ANN model could be a low-cost tool for the determination of ripeness of ‘Hass’ avocado during harvest, storage, and distribution.

Some compressed images using Vector Quantization algorithm suffers from blocking artifacts which degrades the visual appeal of the image. Present study proposes a hybrid vector quantization method applicable on de-correlated color model. As luminance channel carries image information and loss of image information results in degradation of the visual appeal of an image, so aim of this study is focused on retaining more image information during compression process. For luminance channel compression, a new four level quantization based compression method is developed. Luminance channel is partitioned into smaller blocks. Then for each block, four level quantization is applied which local to the current block only. This results many level luminance value effectively for the whole image. It helps to retain better information. Chrominance channels are compressed using conventional Vector Quantization. This hybrid compression method improves visual quality of the decompressed image reasonably compared to VQ. The proposed method is applied on many standard images found in literature and images of UCIDv.2 color image database. Results are analyzed in terms of Peak Signal to Noise Ratio, Structure Similarity Index and space requirement reduction for compressed image using the method. Experimental results show that proposed method retains better quality of image in terms of PSNR and SSIM than Vector Quantization and Modified Vector Quantization. This method reduces storage space requirement for the compressed images in the range of 84% to 89%.

Since digital images are able to be encoded through the luminance-bandwidth-chrominance (YUV) mode, and the contribution of luminance information is greater than that of chrominance information for human visual perception, it can be inferred that the appropriate reduction of chrominance information in convolutional neural network does not disturb image object recognition. In this paper, we propose a new multi-scale non-uniform convolution called YuvConv, wherein the output feature map of the convolutional layer is regarded as an image. First, the output channels in the new convolution are divided into three kinds of components: Y, U, and V tensors. Then, the tensor Y is used to process luminance information, which is high-resolution and occupies more output channels. Next, the tensors U and V are low-resolution and use fewer channels to process chrominance information. Finally, the adjacent tensors (Y-U, Y-U-V, and U-V) are fused as the output of YuvConv. Experimental results indicate that the use of the YuvConv instead of the standard convolution can improve the performance of deep learning tasks, and it can also reduce memory consumption and computation cost.

Recently, learning-based works have been widely-investigated to enhance underwater images. However, interactions between various degradation factors (e.g., color distortion and haze effects) inevitably cause negative interference during the inference phase. Thus, these works cannot fully remove degraded factors. To address this problem, we propose a novel Joint Luminance and Chrominance Learning Network (JLCL-Net). Concretely, we reformulate the task as luminance reconstruction (for haze removal), and chrominance correction (for color correction) sub-tasks by separating the luminance and chrominance (i.e., color appearance) of the underwater images. In this way, we successfully realize the disentanglement in degraded factors to avoid introducing interference. We specify the reconstruction by integrating the atmospheric scattering model, which endows the adaptive dehazing ability over different scenarios. The correction learns to compensate for color by a simple network to reverse the color attenuation process. To this end, we obtain our JLCL-Net. To better train it, we design a new multi-stage cross-space training strategy, which progressively updates the network parameters to enlarge the network potentiality. Extensive evaluations are presented to fully verify our superiority against other methods.

Due to the influence of light absorption and scattering, underwater images usually suffer from quality deteriorations such as color cast and reduced contrast. The diverse quality degradations not only dissatisfy the user expectation but also lead to a significant performance drop in many underwater vision applications. This letter proposes a novel two-branch deep neural network for underwater image enhancement (UIE), which is capable of separately removing color cast and enhancing image contrast by fully leveraging useful properties of the HSV color space in disentangling chrominance and intensity. Specifically, the input underwater image is first converted into the HSV color space and disentangled into HS and V channels to serve as the input of the two branches, respectively. Then, the color cast removal branch enhances the H and S channels with a generative adversarial network architecture while the contrast enhancement branch enhances the V channel via a traditional convolutional neural network. The enhanced channels by the two branches are merged and converted back into RGB color space to obtain the final enhanced result. Experimental results demonstrate that, compared with state-of-the-art UIE methods, our method can produce much more visually pleasing enhanced results.

Although artists' actions in photo retouching appear to be highly nonlinear in nature and very difficult to characterize analytically, we find that the net effects of interactively editing a mundane image to a desired appearance can be modeled, in most cases, by a parametric monotonically non-decreasing global tone mapping function in the luminance axis and by a global affine transform in the chrominance plane that are weighted by saliency. This allows us to simplify the machine learning problem of mimicking artists in photo retouching to constructing a deep artful image transform (DAIT) using convolutional neural networks (CNN). The CNN design of DAIT aims to learn the image-dependent parameters of the luminance tone mapping function and the affine chrominance transform, rather than learning the end-to-end pixel level mapping as in the mainstream methods of image restoration and enhancement. The proposed DAIT approach reduces the computation complexity of the neural network by two orders of magnitude, which also, as a side benefit, improves the robustness and generalization capability at the inference stage. The high throughput and robustness of DAIT lend itself readily to real-time video enhancement as well after a simple temporal processing. Experiments and a Turing-type test are conducted to evaluate the proposed method and its competitors.

In this paper, we investigate the challenging task of removing haze from a single natural image. The analysis on the haze formation model shows that the atmospheric veil has much less relevance to chrominance than luminance, which motivates us to neglect the haze in the chrominance channel and concentrate on the luminance channel in the dehazing process. Besides, the experimental study illustrates that the YUV color space is most suitable for image dehazing. Accordingly, a variational model is proposed in the Y channel of the YUV color space by combining the reformulation of the haze model and the two effective priors. As we mainly focus on the Y channel, most of the chrominance information of the image is preserved after dehazing. The numerical procedure based on the alternating direction method of multipliers (ADMM) scheme is presented to obtain the optimal solution. Extensive experimental results on real-world hazy images and synthetic dataset demonstrate clearly that our method can unveil the details and recover vivid color information, which is competitive among many existing dehazing algorithms. Further experiments show that our model also can be applied for image enhancement.

Vision is the main way for people to perceive and recognize the world. In this paper, four categories of the redundant information of video encoding, spatial redundancy, time redundancy, visual redundancy and statistical redundancy, are first introduced. Spatial redundancy is redundant information existing in static frames, time redundancy is the luminance and chrominance correlation between adjacent frames in the video sequence, visual redundancy is information that the human visual system cannot perceive, statistical redundancy is the expression redundancy of the information source symbols. Then the performance assessment of video coding are introduced. And the future development of video coding and conclusion are finally discussed in this paper.

For images with insufficient visibility, image processing is required. Retinex theory is often implemented for image contrast enhancement for images with this characteristic. This paper proposes a multi-scale retinex-based contrast enhancement method using the illumination component; this method could preserve the naturalness of color images. In the proposed method, illumination was first modified using an illumination modification factor; second, the image was enhanced via adaptive gamma correction. Finally, through the combination of the illumination components of the input image and the adaptive gamma correction image, we ensured the visibility and the naturalness of the output image. To confirm the effectiveness of the proposed method, we compared it with existing contrast enhancement methods. For the experiments, we employed discrete entropy, lightness order error, and mean chrominance error to perform the numerical evaluation. The results indicated that our method was better than a majority of existing methods. Moreover, with regard to the visual evaluation, the naturalness of the image obtained via the proposed method was superior to that of images obtained using other methods.

Person re-identification (re-ID) helps to identify a person’s attention in different cameras. But this is not an easy task, due to distance, illumination and lack of dataset. Nowadays, this field attracts many researchers because of its varied applications. Here, the information of both local texture and global color representations are concatenated with an original raw image. This concatenated information is gathered by finding the maximum value of chrominance in terms of HSV, texture in terms of Scale Invariant Local Ternary Pattern (SILTP) for each pixel and original raw image. SILTP is well known for its illumination invariant texture description. Convolutional Neural Network (CNN) is used in the proposed work to extract the features from the concatenated information. The proposed Prioritized Chromatic Texture Image (PCTimg) is concatenated with original raw image and fed into CNN. Here, finally a six dimensionalfeature is fed into CNN to extract the deep features. Cross-view Quadratic Discriminant Analysis (XQDA) similarity metric algorithm is employed to re-identify a person.Multiscale Retinex algorithm is used for pre-processing the images.To address the challenges in terms of view point deflection, a sliding window is formed for describing local details of a person in the SILTP feature extraction phase. The HSV helps to incorporate the human color perception. The triplet loss function is used to learn the similarity and the dissimilarity of the training images. The performance analysis of the proposed work is improvedwhen compared to the existingworks.

High dynamic range (HDR) imaging enables capturing a wide range of luminance levels existing in real-world scenes. While HDR capturing devices become widespread in the market, the display technology is yet limited in representing full luminance ranges and standard low dynamic range (LDR) displays are currently more prevalent. To visualize the HDR content on traditional displays, tone mapping (TM) operators are introduced that convert HDR content into LDR. The dynamic range compression and different processing steps during TM can lead to loss of scene details, as well as luminance and chrominance changes. Such signal deviations will affect image naturalness and consequently disturb the visual quality of experience. Therefore, research into objective methods for quality evaluation of tone-mapped images has received attention in recent years. In this paper, we proposed a completely blind image quality evaluator for tone-mapped images based on a multi-attribute feature extraction scheme. Due to the diversity of TM distortions, various image characteristics are taken into account to develop an effective metric. The features are designed by considering spectral and spatial entropy, detection probability of visual information, image exposure, sharpness, and color properties. The quality-relevant features are then fed into a machine-learning regression framework to pool a quality score. The validation tests on two benchmark datasets reveal the superior performance of the proposed approach compared to the competing metrics.

In this paper, a multi-exposure image fusion (MEF) method is proposed based on tensor decomposition and saliency model. The main innovation of the proposed method is to explore a tensor domain for MEF and define the fusion rules based on tensor feature of higher order singular value decomposition (HOSVD) and saliency. Specifically, RGB images are converted to YCbCr images to maintain the stability of color information. For luminance channels, luminance patches of luminance images are constructed 3-order sub-tensors, and HOSVD is used to extract features of sub-tensors. Then, the sum of absolute coefficients (SAC) of weight coefficients are defined. Meanwhile, considering the impact of saliency on visual perception, visual saliency maps (VSMs) is used to evaluate luminance patches quality and guide the fusion rules to define the rule of fusion. For chrominance channels, VSMs of the chrominance channels is used to define fused rule. The experimental results show that the fused image with more texture details and saturated color is successfully generated by proposed method.

Video coding in the YCbCr color space has been widely used, since it is efficient for compression, but it can result in color distortion due to conversion error. Meanwhile, coding in the RGB color space maintains high color fidelity, having the drawback of a substantial bitrate increase with respect to YCbCr coding. Cross-component prediction (CCP) efficiently compresses video content by decorrelating color components while keeping high color fidelity. In this scheme, the chroma residual signal is predicted from the luma residual signal inside the coding loop. This paper gives a description of the CCP scheme from several points of view, from theoretical background to practical implementation. The proposed CCP scheme has been evaluated in standardization communities and adopted into H.265/High Efficiency Video Coding (HEVC) Range Extensions. The experimental results show significant coding performance improvements for both natural and screen content video, while the quality of all color components is maintained. The average coding gains for natural video are 17% and 5% bitrate reduction in the case of intra coding and 11% and 4% in the case of inter coding for RGB and YCbCr coding, respectively, while the average increment of encoding and decoding times in the HEVC reference software implementation are 10% and 4%, respectively.

Most of today’s techniques encrypt all of the image data, which consumes a tremendous amount of time and computational payload. This work introduces a selective image encryption technique that encrypts predetermined bulks of the original image data in order to reduce the encryption/decryption time and thecomputational complexity of processing the huge image data. This technique is applying a compression algorithm based on Discrete Cosine Transform (DCT). Two approaches are implemented based on color space conversion as a preprocessing for the compression phases YCbCr and RGB, where the resultant compressed sequence is selectively encrypted using randomly generated combined secret key.The results showed a significant reduction in image quality degradation when applying the system based on YCbCr over RGB, where the compression ratio was raised in some of the tested images to 50% for the same Peak Signal to Noise Ratio (PSNR). The usage of 1-D DCT reduced the transform time by 47:1 times comparedto the same transform using 2-D DCT. The values of the adaptive scalar quantization parameters were reduced to the half for the luminance (Y band) to preserve the visual quality, while the chrominance (Cb and Cr bands) were quantized by the predetermined quantization parameters. In the hybrid encoder horizontal zigzag,block scanning was applied to scan the image. The Detailed Coefficient (DC) coefficients are highly correlated in this arrangement- where DC are losslessly compressed by Differential Pulse Coding Modulation (DPCM) and theAccumulative Coefficients (AC) are compressed using Run Length Encoding (RLE). As a consequence, for the compression algorithm, the compression gain obtained was up to 95%. Three arrays are resulted from each band (DC coefficients, AC values, and AC runs), where the cipher is applied to some or all of those bulksselectively. This reduces the encryption decryption time significantly, where encrypting the DC coefficients provided the second best randomness and the least encryption/decryption time recorded (3 10-3 sec.) for the entire image. Although the compression algorithm consumes time but it is more efficient than the savedencryption time.

With the increasing demands of end-users to the visual perception in three-dimension (3D) image, quality assessment for 3D imageis dominantly required as the feedback information for multimedia transmission systems. In this paper, a novel full-reference quality assessment method by considering the depth and integral color information of 3D image under cloud computing environment is proposed. Based on the property of the depth information in 3D image, the depth map is firstly separated into different planes according to the perception of human visual system (HVS). Then, after express the image pixels of every separated plane through quaternions, the structural and energy information are separated by quaternion singular value decomposition (QSVD). The distortion of structural and energy in every plane are calculated in various formulas respectively. The final result is calculated in terms of the global score, which synthesizes the structural and energy distortion scores in every individual depth plane. It should be pointed out that the chrominance information is employed in our mechanism to evaluate the color image quality because of its useful characteristic for 3D color image quality assessment, and its spatial correlation is used for calculating structural distortion through vector cross-product. Our experimental results confirm that the proposed method has achieves better performance under cloud computing environments compared with other existing 3D image quality assessment methods.

In recent years, generative adversarial networks (GANs) have been widely used to generate realistic fake face images, which can easily deceive human beings. To detect these images, some methods have been proposed. However, their detection performance will be degraded greatly when the testing samples are post-processed. In this paper, some experimental studies on detecting post-processed GAN-generated face images find that (a) both the luminance component and chrominance components play an important role, and (b) the RGB and YCbCr color spaces achieve better performance than the HSV and Lab color spaces. Therefore, to enhance the robustness, both the luminance component and chrominance components of dual-color spaces (RGB and YCbCr) are considered to utilize color information effectively. In addition, the convolutional block attention module and multilayer feature aggregation module are introduced into the Xception model to enhance its feature representation power and aggregate multilayer features, respectively. Finally, a robust dual-stream network is designed by integrating dual-color spaces RGB and YCbCr and using an improved Xception model. Experimental results demonstrate that our method outperforms some existing methods, especially in its robustness against different types of post-processing operations, such as JPEG compression, Gaussian blurring, gamma correction, and median filtering.