Chrominance Components Research Articles

Multi-modal medical image fusion using improved dual-channel PCNN.

This paper proposes a medical image fusion method in the non-subsampled shearlet transform (NSST) domain to combine a gray-scale image with the respective pseudo-color image obtained through different imaging modalities. The proposed method applies a novel improved dual-channel pulse-coupled neural network (IDPCNN) model to fuse the high-pass sub-images, whereas the Prewitt operator is combined with maximum regional energy (MRE) to construct the fused low-pass sub-image. First, the gray-scale image and luminance of the pseudo-color image are decomposed using NSST to find the respective sub-images. Second, the low-pass sub-images are fused by the Prewitt operator and MRE-based rule. Third, the proposed IDPCNN is utilized to get the fused high-pass sub-images from the respective high-pass sub-images. Fourth, the luminance of the fused image is obtained by applying inverse NSST on the fused sub-images, which is combined with the chrominance components of the pseudo-color image to construct the fused image. A total of 28 diverse medical image pairs, 11 existing methods, and nine objective metrics are used in the experiment. Qualitative and quantitative fusion results show that the proposed method is competitive with and even outpaces some of the existing medical fusion approaches. It is also shown that the proposed method efficiently combines two gray-scale images.

Enhancing Visibility Through Image Defogging

This project introduces a method for enhancing visibility in foggy images through histogram equalization. By leveraging the LAB color space, the method separates luminance and chrominance components, applying histogram equalization solely to the luminance channel. This process effectively enhances contrast and brightness while preserving color fidelity. Experimental results demonstrate significant visibility improvements in foggy scenes, with computational efficiency suitable for real-time applications. This approach holds promise for enhancing visibility in critical domains like transportation safety and outdoor surveillance. Keywords: Fog Removal, Atmospheric Haze, Luminance Channel, Image Enhancement, Noise Reduction ,Histogram Equalisation, Color Fidelity.

JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer.

JPEG compression adopts the quantization of Discrete Cosine Transform (DCT) coefficients for effective bit-rate reduction, whilst the quantization could lead to a significant loss of important image details. Recovering compressed JPEG images in the frequency domain has recently garnered increasing interest, complementing the multitude of restoration techniques established in the pixel domain. However, existing DCT domain methods typically suffer from limited effectiveness in handling a wide range of compression quality factors or fall short in recovering sparse quantized coefficients and the components across different colorspaces. To address these challenges, we propose a DCT domain spatial-frequential Transformer, namely DCTransformer, for JPEG quantized coefficient recovery. Specifically, a dual-branch architecture is designed to capture both spatial and frequential correlations within the collocated DCT coefficients. Moreover, we incorporate the operation of quantization matrix embedding, which effectively allows our single model to handle a wide range of quality factors, and a luminance-chrominance alignment head that produces a unified feature map to align different-sized luminance and chrominance components. Our proposed DCTransformer outperforms the current state-of-the-art JPEG artifact removal techniques, as demonstrated by our extensive experiments.

DFCE: Decoder-Friendly Chrominance Enhancement for HEVC Intra Coding

We propose a decoder-friendly chrominance enhancement method for the compressed images. Our proposed method is developed based on the luminance-guided chrominance enhancement network (LGCEN) and online learning. With LGCEN, the textures of the compressed chrominance components are enhanced by the guidance of luminance component. Moreover, LGCEN is constructed with the recursive design and the light-weight channel attention mechanism to achieve high performance as well as low complexity. It is arranged at both encoder and decoder sides. Given the input image, we train LGCEN at encoder side by using online learning. With online learning, we partially update network parameters and transmit them to decoder to update LGCEN arranged there. The adoption of online learning effectively reduces the workload of decoder and guarantee high robustness. Compared with the state-of-the-art methods, our proposed approach achieves superior performance.

Detection of Recolored Image by Texture Features in Chrominance Components

Image recoloring is an emerging editing technique that can change the color style of an image by modifying pixel values without altering the original image content. With the rapid proliferation of social network and image editing techniques, recolored images (RIs) have raised new security issues in society. Existing detection methods have good performance in detecting RIs for certain categories of recoloring techniques. However, the performance on the handcrafted recoloring scenario is still poor due to the influence of human prior knowledge. To deal with this problem, we explore a solution from the perspective of chrominance texture artifacts to improve the generalization ability. The results of the analysis show that natural images (NIs) and RIs have textural disparities in different color components, especially in the chrominance components (i.e., Cb, Cr, and H). Based on such new prior knowledge of statistical discriminability, we propose a feature set to capture texture features in chrominance components for identifying RIs. Extensive experimental results show that the proposed method can accurately identify RIs with certain categories of recoloring techniques, and outperforms existing methods in the scenario of handcrafted recoloring.

Underwater image enhancement by modified color correction and adaptive Look-Up-Table with edge-preserving filter

Particles suspended in water cause poor visibility and color distortions in the images that are captured in underwater. These suspended particles cause light to be scattered that results in non-uniform illumination due to dispersion and refraction of light. The illumination variation cause color casts in the underwater images. This paper proposes an underwater image enhancement method that improves the visibility, contrast and color cast in the underwater images. The proposed method includes a modified color correction, an adaptive Look-Up-Table (LUT) and an edge-preserving filter. First, a color correction is performed on individual LAB color space components. The improvement of the luminance (L) component is processed with a sequence of steps such as CLAHE, sharpening, gamma and color correction. In addition, the chrominance (A and B) components are modified by further color correction. Second, the contrast of the color-corrected image is enhanced using the adaptive LUT based on probability threshold. This probability threshold increases the image contrast by limiting the pixel intensity values in each RGB channel. In addition, the probability threshold controls the halo effects and artifacts. Finally, the underwater image is enhanced with a fast local Laplacian filter (FLLF) to preserve the edge details with texture smoothing. The denoised image is adjusted with histogram normalization for a further color balancing. The experimental results obtained from the proposed method on different underwater image datasets shows a supremacy over color casts, visibility, halo effects, free of artifacts and edge-preserving in the underwater images compared to the existing methods.

Multi-Layer Decomposition and Synthesis of HDR Images to Improve High-Saturation Boundaries

Recently, high dynamic range (HDR) imaging has been used in many fields such as display, computer graphics, and digital cameras. Various tone mapping operators (TMOs) are used for effective HDR imaging. TMOs aim to express HDR images without loss of information and natural images on existing display equipment. In this paper, to improve the color distortion that occurs during tone mapping, multi-layer decomposition-based color compensation and global color enhancement of the boundary region are proposed. Multi-layer decomposition is used to preserve the color information of the input image and to find the area where color distortion occurs. Color compensation and enhancement are especially used to improve the color saturation of the border area, which is degraded due to color distortion and tone processing. Color compensation and enhancement are processed in IPT color space with excellent hue linearity to improve effective performance by reducing interference between luminance and chrominance components. The performance of the proposed method was compared to the existing methods using naturalness, distortion, and tone-mapped image quality metrics. The results show that the proposed method is superior to the existing methods.

Application of Multimedia Semantic Extraction Method in Fast Image Enhancement Control

In order to solve the problem that it is difficult to effectively enhance the details of the compressed domain and maintain the overall brightness and clarity of the image when improving the image contrast in the current image enhancement method in the compressed domain, a multimedia semantic extraction method is applied in fast image enhancement control. It has been proposed that thealgorithm that synthesizes training samples according to the Retinex model converts the original low-light image from RGB (red-green-blue) space to HSI (hue saturation intensity) color space, keeps the chrominance and saturation components unchanged, and uses DCNN to enhance the luminance component; finally, it converts the HSI color space to RGB space to get the final enhanced image. The experimental results show that the performance of the model will increase with the increase of the number of convolution kernels, but the increase of the number of convolution kernels will undoubtedly increase the amount of calculation; it can also be found that when the number of network layers is 7, the PSNR of the image output by the model increases. The highest value, increasing the number of network layers, does not necessarily improve the performance of the model; with or without BN, his training method converges more easily than direct RGB image enhancement, with higher average PSNR and SSIM values. The experimental results show that, compared with the traditional Retinex enhancement algorithm and the DCT compression domain enhancement algorithm, the algorithm has better detail enhancement and color preservation effects and can better suppress the block effect.

Robust video steganography for social media sharing based on principal component analysis

Most social media channels are lossy where videos are transcoded to reduce transmission bandwidth or storage space, such as social networking sites and video sharing platforms. Video transcoding makes most video steganographic schemes unusable for hidden communication based on social media. This paper proposes robust video steganography against video transcoding to construct reliable hidden communication on social media channels. A new strategy based on principal component analysis is provided to select robust embedding regions. Besides, side information is generated to label these selected regions. Side information compression is designed to reduce the transmission bandwidth cost. Then, one luminance component and one chrominance component are joined to embed secret messages and side information, notifying the receiver of correct extraction positions. Video preprocessing is conducted to improve the applicability of our proposed method to various video transcoding mechanisms. Experimental results have shown that our proposed method provides stronger robustness against video transcoding than other methods and achieves satisfactory security performance against steganalysis. Compared with some existing methods, our proposed method is more robust and reliable to realize hidden communication over social media channels, such as YouTube and Vimeo.

Study on the Application of Visual Communication Design in APP Interface Design in the Context of Deep Learning.

Visual communication concepts enable linguistics or semiotics to the teaching of visual communication designs, creating graphic designs into an innovative and scientific discipline. The use of storyline techniques in visual communication not only inspires the imagination of designer but also arouses the visual memory of the audience. Besides, improving cultural heritage such as historical images is important to protect cultural diversity. Recently, the developments of deep learning (DL) and computer vision (CV) approaches make it possible for the automatic colorization of grayscale images into color images. Also, the usage of visual communication design in APP interface design has increased. With this motivation, this work introduces the enhanced deep learning-based automated historical image colorization (EDL-AHIC) technique for wireless network-enabled visual communication. The proposed EDL-AHIC technique intends to effectually convert the grayscale images into color images. The presented EDL-AHIC technique extracts the local as well as global features. For global feature extraction, the enhanced capsule network (ECN) model is applied. Finally, the fusion layer and decoding unit are employed to determine the output, i.e., chrominance component of the input image. A comprehensive experimental validation process is performed to ensure the betterment of the EDL-AHIC technique. The comparison study reported the supremacy of the EDL-AHIC technique over the other recent methods.

A Robust GAN-Generated Face Detection Method Based on Dual-Color Spaces and an Improved Xception

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.

Multi-scale residual attention network for single image dehazing

Single image dehazing is a fundamental but difficult task in image processing. Although various deep learning-based methods have achieved good dehazing performance, it is still a challenge to recover a haze-free image without color distortion from a hazy image. The color performance of nowadays dehazing methods needs further improvement. In this paper, the effect of haze on the luminance and chrominance of CIELAB color space is firstly studied. Based on CIELAB color space, a novel multi-scale residual attention network (MSRA-Net) is proposed for single image dehazing. The proposed network consists of two subnetworks: one is designed to remove haze from the luminance channel; another focuses on enhancing the chrominance components and makes the color more realistic. To improve the performance of MSRA-Net, the multi-scale residual attention block (MSRA-block) and feature aggregation building block (FAB-block) are presented. Both the subjective and objective performance show that the proposed method outperforms the existing dehazing algorithms, especially in terms of color performance.

Color Based Object Categorization Using Histograms of Oriented Hue and Saturation

In the last few years, there has been a lot of interest in making smart components, e.g. robots, able to simulate human capacity of object recognition and categorization. In this paper, we propose a new revolutionary approach for object categorization based on combining the HOG (Histograms of Oriented Gradients) descriptors with our two new descriptors, HOH (Histograms of Oriented Hue) and HOS (Histograms of Oriented Saturation), designed it in the HSL (Hue, Saturation and Luminance) color space and inspired by this famous HOG descriptor. By using the chrominance components, we have succeeded in making the proposed descriptor invariant to all lighting conditions changes. Moreover, the use of this oriented gradient makes our descriptor invariant to geometric condition changes including geometric and photometric transformation. Finally, the combination of color and gradient information increase the recognition rate of this descriptor and give it an exceptional performance compared to existing methods in the recognition of colored handmade objects with uniform background (98.92% for Columbia Object Image Library and 99.16% for the Amsterdam Library of Object Images). For the classification task, we propose the use of two strong and very used classifiers, SVM (Support Vector Machine) and KNN (k-nearest neighbors) classifiers.

Fast block partitioning scheme for chrominance intra prediction of versatile video coding standard

We present a fast block partitioning scheme for chrominance intra prediction of the Versatile Video Coding (VVC) standard. VVC adopted several innovative tools to improve the coding performance compared with its predecessor standard, enabling the efficient handling of emerging video content such as ultra-high definition (UHD). These coding performance improvements come at the cost of a significant increase in coding complexity, mainly due to the new block partitioning structure called quadtree with nested multi-type tree (QTMT), which can be applied separately for luminance and chrominance components. Thus, this work proposes a scheme focusing on chrominance intra coding, composed of chrominance splitting early termination based on luminance QTMT and fast chrominance split decision based on the sub-block variances. Experimental results demonstrated that the proposed scheme provides 60.03% and 8.18% of chrominance and total encoding time savings, respectively, with a negligible impact of 0.66% in the YUV-BDBR.

Determination of ‘Hass’ avocado ripeness during storage by a smartphone camera using artificial neural network and support vector regression

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.

Combining Neural Networks for Skin Detection

Two types of combining strategies were evaluated namely combining skin features and combining skin classifiers. Several combining rules were applied where the outputs of the skin classifiers are combined using binary operators such as the AND and the OR operators, Voting, Sum of Weights and a new neural network. Three chrominance components from the YCbCr colour space that gave the highest correct detection on their single feature MLP were selected as the combining parameters. A major issue in designing a MLP neural network is to determine the optimal number of hidden units given a set of training patterns. Therefore, a coarse to fine search method to find the number of neurons in the hidden layer is proposed. The strategy of combining Cb/Cr and Cr features improved the correct detection by 3.01% compared to the best single feature MLP given by Cb-Cr. The strategy of combining the outputs of three skin classifiers using the Sum of Weights rule further improved the correct detection by 4.38% compared to the best single feature MLP.

Gradient-Based Intraprediction Fusion for Video Coding

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.

An Efficient Algorithm for Luminance Optimization in Chroma Downsampling

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.

Single image dehazing by dark channel prior and luminance adjustment

ABSTRACT This paper presents a new and simple algorithm to eliminate haze from a single image using YIQ colour space and Dark Channel Prior (DCP). The suggested method consists of two parts. The first one is enhancement by adjusting contrast and lightness using contrast-limited adaptive histogram equalization and gamma transform. The second part involves applying DCP on a hazy image using the RGB colour model to achieve a haze-free image. The resulting image is then converted into YIQ, and the chrominance components (I and Q) are extracted and combined with the enhanced luminance component (Y) to achieve an enhancement haze-free image. The suggested method is applied to many outdoor images, and the quality of the resulting images is compared with that of images from other methods, by using several measures of quality. Analysing the results explain that the suggested method has a higher quality value than other methods.

Evaluation of Applicability of Various Color Space Techniques of UAV Images for Evaluating Cool Roof Performance

Global warming is intensifying worldwide, and urban heat islands are occurring as urbanization progresses. The cool roof method is one alternative for reducing the urban heat island phenomenon and lowering the heat on building roofs for a comfortable indoor environment. In this study, a cool roof evaluation was performed using an unmanned aerial vehicle (UAV) and a red, green and blue (RGB) camera instead of a laser thermometer and a thermal infrared sensor to evaluate existing cool roofs. When using a UAV, an RGB sensor is used instead of expensive infrared sensor. Various color space techniques, namely light-reflectance value, hue saturation value (HSV), hue saturation lightness, and YUV (luma component (Y) and two chrominance components, called U (blue projection) and V (red projection)) derived from RGB images, are applied to evaluate color space techniques suitable for cool roof evaluation. This case study shows the following quantitative results: among various color space techniques investigated herein, the white roof with lowest temperature (average surface temperature: 44.1 °C; average indoor temperature: 33.3 °C) showed highest HSV, while the black roof with the highest temperature (surface temperature average: 73.4 °C; indoor temperature average: 37.1 °C) depicted the lowest HSV. In addition, the HSV showed the highest correlation in both the Pearson correlation coefficient and the linear regression analyses when the correlation among the brightness, surface temperature, and indoor temperature of the four color space techniques was analyzed. This study is considered a valuable reference for using RGB cameras and HSV color space techniques, instead of expensive thermal infrared cameras, when evaluating cool roof performance.