Improvement of JPEG Compression Efficiency Using Information Hiding and Image Restoration

This paper concerns color image restoration aiming at objective quality improvement of compressed color images in general rather than merely artifact reduction. In compressed color images, colors are usually represented by luminance and chrominance components. Considering characteristics of human vision system, chrominance components are generally represented more coarsely than luminance component. To recover such chrominance components, we previously proposed a model-based chrominance restoration algorithm where color images are modeled by a Markov random field. This paper presents a color image restoration algorithm derived by the MAP estimation where all components are totally estimated. Experimental results show that the proposed restoration algorithm is more effective than the previous one.

In this paper, a wavelet-based watermarking scheme for color images is proposed. The watermarking scheme is based on the design of a color visual model that is the modification of a perceptual model used in the image coding of gray scale images. The model is designed to estimate and utilize the noise detection threshold of each wavelet coefficient in luminance and chrominance components in order to satisfy the transparency and robustness required by color image watermarking techniques. Besides the visual masking given by taking into account the luminance content and the texture, the crossed masking effect and the effect given by the variance within the local region of the target coefficient are investigated such that the estimated noise detection threshold is more close to the real noise detection threshold. In a locally adaptive fashion based on the wavelet decomposition, the thresholds of subband coefficients in each color component are derived to construct a perceptually lossless quantization matrix for embedding watermarks. Performance in terms of robustness and transparency is obtained by embedding the maximum strength watermark while maintaining the perceptually lossless quality of the watermarked color image. Simulation results show that the proposed scheme is more robust than the existing schemes while retaining the watermark transparency.

Region extraction and labeling is a fundamental process in building computer systems that automatically analyze and interpret images and image sequences of a scene. It involves extracting meaningful regions and their attributes from an image that are of interest in a given domain, e.g., a human face in face recognition and surveillance applications, a vehicle in intelligent highway monitoring, or an industrial tool in automated manufacturing and inspection applications. In this paper, we present a generalized and fast region labeling algorithm that can be applied to numerous image processing tasks: region extraction in binary images, region segmentation from gray scale or color images, region boundary extraction, chain code extraction, run-length coding of images, and image restoration from chain codes. The general applicability of our algorithm stems from its strengths in identifying the fundamental and common computational steps and providing robust and efficient procedures to accomplish these steps without requiring multiple scans of images. Our algorithm is shown to be more effective in terms of space and computational efficiency, in comparison with many existing approaches to region labeling and extraction. Results from our experiments with over 5,000 frames obtained from MPEG-1 video bitstreams demonstrate the good performance of our algorithm in terms of video text segmentation accuracy and computational efficiency.

Application-specific extensions of a processor provide an efficient mechanism that meets the growing performance and power demands of portable embedded applications such as interactive multimedia communications. This work investigates the use of both the luminance and chrominance components in color image- and video-processing applications and proposes a color-aware instruction set extension (CAX) that improves the performance of color multimedia-processing applications. The CAX ISA is able to exploit a new level of subword parallelism not reachable by current multimedia extensions. Unlike typical multimedia extensions (e.g., Intel MMX, MIPS MDMX, and AMD 3DNow!), CAX instructions support parallel operations on two-packed 16-bit (6:5:5) YCbCr color pixels in a 32-bit datapath width and obtain substantial performance and code density improvements through direct support for color data processing rather than depending solely on generic subword parallelism. The results on a set of media benchmark applications indicate that the CAX versions achieve a speedup ranging from 3x to 5.8x while reducing energy consumption from 68% to 83% over the baseline versions on an identically-configured, dynamically-scheduled superscalar processor. The CAX versions also outperform the MDMX (MIPS multimedia extension) performance.

The literature has introduced many steganalysis methods intended to com- bat specific steganography techniques and to detect particular image formats. This paper proposes a detection system based on extracting histogram features. The features are extracted by exploiting the histogram of difference image, which is usually a generalised Gaussian distribution centred at 0. The histogram of difference image and the renormal- ized histogram are created for clean and stego images, therefore using the peak value and renormalized histogram as features for classification. To obtain the difference between neighbouring pixels, the difference images are computed for four directions (vertical, hor- izontal, diagonal, and anti-diagonal). The renormalized histogram of the difference im- age is created a number of times (n) for the four directions. This work implements two commonly-used steganography methods: the Least Significant Bit (LSB) and F5 al- gorithm to create a large database of stego images for system evaluation. Colour and grey images with different formats are chosen for training and testing the system. These formats are lossless and lossy compressions, with all features extracted from each colour channel (RGB) separately. The size of hidden files plays an important role in terms of detection. Therefore, to improve the proposed systems detection capacity, different sizes of hidden files have been considered. The proposed detection system was trained and tested to distinguish stego images from clean ones using the Discriminant Analysis (DA) classification method and Multilayer Perceptron neural network (MLP). The ex- perimental results prove that the proposed system possesses reliable detection ability and accuracy. The chosen classification methods show dissimilar performance in terms of classifying grey and colour images. The system holds more generalisability than previous systems by covering different types of stego images, image formats and hidden file sizes. In addition, extensive experimental results show that the proposed steganalysis system outperforms some previous detection methods.

A new background estimation method is proposed to improve iterative restoration of microscope images. The method is based on a sequence of subtractions performed in the beginning of an iterative restoration procedure. A series of experiments using background intensity detection methods were carried out in order to analyse the influence of a correct estimation of the background. The restoration results as a function of the detected background value is discussed. The results indicates a strong influence of the estimate on the restoration of images. When compared to an optimal error-based method, the proposed method was faster and obtained a good approximation to the correct value.

Satellites have many high-, medium-, and low-frequency micro vibration sources that lead to the optical axis jitter of the optical load and subsequently degrade the remote sensing image quality. To address this problem, this paper developed an image motion detection and restoration method based on an inertial reference laser, and describe edits principle and key components. To verify the feasibility and performance of this method, this paper also built an image motion measurement and restoration system based on an inertial reference laser, which comprised a camera (including the inertial reference laser unit and a Hartmann wavefront sensor), an integrating sphere, a simulated image target, a parallel light pope, a vibration isolation platform, a vibration generator, and a 6 degrees of freedom platform. The image restoration principle was also described. The background noise in the experiment environment was measured, and an image motion measurement accuracy experiment was performed. Verification experiments of image restoration were also conducted under various working conditions. The experiment results showed that the error of image motion detection based on the inertial reference laser was less than 0.12 pixels (root mean square). By using image motion data to improve image quality, the modulation transfer function (MTF) of the restored image was increased to 1.61–1.88 times that of the original image MTF. The image motion data could be used as feedback to the fast steering mirror to compensate for the satellite jitter in real time and to directly obtain high-quality images.

Most existing methods for edge detection rely on contrast dependent thresholds. We show that a local measurement defined by the ratio of the smallest to the largest eigenvalue of the second moment matrix of filter kernels, can be used to separate smooth, low curvature curves and straight lines from noise, independent of contrast, in both color and gray level images. This is done without applying a threshold to the gradient magnitude. The edge images are defined as zero crossings in the gradient direction. The covariance matrix can easily be computed for both gray level images and color images. Further we show the potentiality of such a measure by integrating it with the Hough transform to extract long straight lines in noisy color images. The method is shown to successfully extract consistent line features from color images of a scene, captured under drastically different lightening conditions.

We propose an effective facial features detection method for human– robot interaction (HRI) with indoor mobile robot. In case of human-robot interaction, its vision system has to manage such difficult problems as pose variations, illumination changes, and complex backgrounds, which is caused by the mobility of robot. In this paper, in order to overcome such difficult problems, we suggest a facial feature detection method based on local image area and direct pixel-intensity distributions, in which we propose two novel concepts; the directional template for evaluating intensity distributions and the edge-like blob map with multiple strength intensity. Using this proposed blob map, we show that the locations of major facial features – two eyes and a mouth – can be successfully estimated. Without the information of facial area boundary, final candidate face region is determined by both obtained locations of facial features and weighted correlations with stored facial templates. Our approach is also a flexible algorithm that can be applicable to both color and gray image. In case of color image, faster detection of both facial features and face is feasible by using the chromatic property of facial color. Experimental results from many color images and well-known gray level face database images prove the usefulness of proposed algorithm in human– robot interaction applications.

Steganography is the art of the hidden data in such a way that it detection of hidden knowledge prevents. As the necessity of security and privacy increases, the need of the hiding secret data is ongoing. In this paper proposed an enhanced detection of the 1-2-4 LSB steganography and RSA cryptography in Gray Scale and Color images. For color images, we apply 1-2-4 LSB on component of the RGB, then encrypt information applying RSA technique. For Gray Images, we use LSB to then encrypt information and also detect edges of gray image. In the experimental outcomes, calculate PSNR and MSE. We calculate peak signal noise ratio for quality and brightness. This method makes sure that the information has been encrypted before hiding it into an input image. If in any case the cipher text got revealed from the input image, the middle person other than receiver can't access the information as it is in encrypted form.

Color images have a wider range of applications than gray images. There are two ways to extend the traditional super-resolution reconstruction method to color images: Super resolution reconstructs each channel of the color image individually; Change the RGB color bands into YCrCb color bands, then super-resolution reconstructs the luminance component and interpolates the chrominance components.These algorithms cannot effectively utilize the property that the edges and textures are similar in the RGB channels, and the results of those methods may lead to color artifacts. Aiming to solve these problems, we propose a new super-resolution method based on cross channel prior. First, a cross channel prior is proposed to describe the similarity of gradient in RGB channels. Then, a new super-resolution method is proposed for color images via combination of the cross channel prior and the traditional super-resolution methods. Finally, the proposed method reconstructs the color channels alternately. The experimental results show that the proposed method could effectively suppress the generation of color artifacts and improve the quality of the reconstructed images.

This paper presents an effective system with the aim of solving the problem of image parsing. In order to make full use of information and characteristics in the input image(colored), the gray image is calculated from the color image, and the superpixels of both the gray image and the color one are obtained. After feature extraction, the features abstracted from color image and gray image are fused together to construct the final features. Then, the likelihood ratio scores of the input image and the similar images in the retrieval set are calculated according to these final features and the final labeling result are obtained through MRF analysis. The effectiveness of the proposed system is tested on the public dataset of SIFT Flow and experimental result shows that the proposed method in this paper is efficient.

In this paper, we propose a new technique for halftoning color images. Our technique parallels recent work in model-based halftoning for both monochrome and color images; we incorporate a human visual model that accounts for the difference in the responses of the human viewer to luminance and chrominance information. Thus, the RGB color space must be transformed to a luminance/chrominance based color space. The color transformation we use is a linearization of the uniform color space L<SUP>*</SUP>a<SUP>*</SUP> b<SUP>*</SUP> which also decouples changes between the luminance and chrominance components. After deriving a tractable expression for total- squared perceived error, we then apply the method of Iterated Conditional Modes (ICM) to iteratively toggle halftone values and exploit several degrees of freedom in reducing the perceived error as predicted by the model.

The color-embedded-grayscale image aims to transform a color image into its grayscale version. It involves the Discrete Wavelet Transformation (DWT) and chrominance-luminance color space. This technique hides the color information contained in the chrominance component into the DWT transformed of luminance component. The color-embedded-grayscale image can be processed to yield a recovered color image. However, the quality of this recovered image is less acceptable compared to the original version. Thus, this paper improves this recovered image using deep learning approach, i.e. Convolutional Neural Network (CNN). As documented in the experiments, the proposed method is superior in comparisons to the other existing schemes.

Color image restoration is a fundamental task in computer vision and image processing, with extensive real‐world applications. In practice, color images often suffer from degradations caused by sensor noise, optical blur, compression artifacts, and data loss during the acquisition, transmission, or storage. Unlike grayscale images, color images exhibit high correlations among their RGB channels. Directly extending grayscale restoration methods to color images often leads to issues such as color distortion and structural artifacts. To address these challenges, this paper proposes a novel quaternion‐based color image restoration framework. The method integrates low‐rank pseudo‐norm constraints with saturation‐value total variation (SVTV) regularization, effectively enhancing restoration quality in tasks including denoising, deblurring, and inpainting of degraded color images. The proposed algorithm is efficiently solved using the alternating direction method of multipliers (ADMM), and restoration performance is rigorously evaluated through quantitative metrics including peak signal‐to‐noise ratio (PSNR), structural similarity index measure (SSIM), and S‐CIELAB error. Extensive experimental results demonstrate the superior performance of our method compared to existing approaches.