Binocular Just Noticeable Difference Research Articles

A color image watermarking framework for copyright protection of stereo images based on binocular just noticeable difference and LU decomposition

The copyright protection of three-dimensional (3D) content is a matter of interest in artistic and creative works due to the rights of the holder for the distribution of the material. However, although stereo images are widely used for the generation of 3D content, there is a little amount of research focused on copyright protection for this type of image. In this study, a novel invisible and blind color image watermarking framework for protecting the copyright of the stereo images based on Binocular Just Noticeable Difference (BJND) and lower-and-upper (LU) decomposition is proposed. In this framework, a color watermark is encoded to reduce the information. Then, the BJND model is calculated in the R channel of a stereo image, and finally, LU decomposition is applied into the G and B channels to embed and extract a color watermark. The BJND model has demonstrated a remarkable sensitivity comparable with the Human Visual System (HVS) in luminance changes such as the ones presented in stereo images, guaranteeing a good protection performance and a high imperceptibility. The proposed framework was compared with other state-of-the-art techniques, and it has demonstrated better performance and high resistance to attacks of JPEG compression or impulsive and Gaussian noise. Additionally, this novel framework does not affect the generation of the disparity map or the 3D content when the watermarked stereo images are used for their creation.

Perceptually Driven Nonuniform Asymmetric Coding of Stereoscopic 3D Video

Asymmetric stereoscopic video coding has already proven its effectiveness in reducing the bandwidth required for stereoscopic 3D delivery without degrading the visual quality. This approach, in which the left and right views are encoded with different levels of quality, relies on the perceptual theory of binocular suppression. However, to ensure comfortable 3D viewing, the just-noticeable level of asymmetry, i.e., the maximum quality gap between views, has to be carefully defined. Both subjectively and empirically fixed thresholds of asymmetry demonstrated either the maladjustment to content or dependency to the experimental design. This paper describes a new nonuniform asymmetric stereoscopic video coding method adaptively adjusting the level of asymmetry for each region of the image based on its perceptual significance. The proposed method uses a fully automated model that dynamically determines the best bounds of asymmetry for which the 3D viewing experience will not be altered. This is achieved by exploiting several human-visual-system-inspired models, namely, the binocular just-noticeable difference, and the visual saliency map and depth information. The simulation results show that the proposed method results in bit rate saving of up to 26% and provides better 3D visual quality compared with state-of-the-art asymmetric coding methods.

Enhancement of low light level images using color-plus-mono dual camera.

In digital photography, the improvement of imaging quality in low light shooting is one of the users' needs. Unfortunately, conventional smartphone cameras that use a single, small image sensor cannot provide satisfactory quality in low light level images. A color-plus-mono dual camera that consists of two horizontally separate image sensors, which simultaneously captures both a color and mono image pair of the same scene, could be useful for improving the quality of low light level images. However, an incorrect image fusion between the color and mono image pair could also have negative effects, such as the introduction of severe visual artifacts in the fused images. This paper proposes a selective image fusion technique that applies an adaptive guided filter-based denoising and selective detail transfer to only those pixels deemed reliable with respect to binocular image fusion. We employ a dissimilarity measure and binocular just-noticeable-difference (BJND) analysis to identify unreliable pixels that are likely to cause visual artifacts during image fusion via joint color image denoising and detail transfer from the mono image. By constructing an experimental system of color-plus-mono camera, we demonstrate that the BJND-aware denoising and selective detail transfer is helpful in improving the image quality during low light shooting.

An adaptive contrast enhancement method for stereo endoscopic images combining binocular just noticeable difference model and depth information

Endoscopic image enhancement has become a very popular research field due to the success of minimally invasive interventions and the innovation of new technological treatment and diagnosis tools such as stereoscopic laparoscopes and the wireless capsule endoscopy. In spite of the important advances achieved in terms of image processing and enhancement, only a few techniques can be adapted to stereo endoscopic images. This can be explained by the specificities of the stereo endoscopic video acquisition process, the surgical tasks artifacts and the endoscopic domain characteristics (e.g., organ textures, edges, color distribution). In this paper we present a contrast enhancement method for stereo endoscopic images taking into consideration some of these specificities, namely those of the acquired stereo images i.e. the depth information, the binocular vision and the organs boundaries/textures. The idea is to enhance the image quality by a contrast enhancement process that exploits the local image activity, the depth information and the binocular just noticeable difference (BJND) model. The results of the conducted subjective experiment show that the proposed method produces stereo endoscopic images with sharper details of the underlying tissues and organs, without introducing any halo effect or overshooting. The observers reported as well a more depth feeling and less visual fatigue when perceiving the enhanced stereo endoscopic images.

Stereoscopic Visual Attention Guided Seam Carving for Stereoscopic Image Retargeting

Stereoscopic image retargeting plays an important role in adaptive 3D stereoscopic displays. It aims to fit displays with various resolutions while preserving visually salient content and geometric consistency. We propose a stereoscopic image retargeting method based on stereoscopic visual attention guided seam carving. Firstly, stereoscopic saliency map is generated by combining 2D saliency and depth saliency maps, and significant energy map is generated by considering binocular disparity binocular and binocular just-noticeable-difference (BJND). Then, seam selection is applied to the left image based on stereoscopic saliency and energy maps, and seam replacement is performed for the occluded regions to prevent the geometry inconsistency. Finally, according to the matched left and right seams, the retargeted stereoscopic image is generated. In the experiments, subjective and objective analysis on three stereoscopic image databases shows that the proposed approach produces better seam carving results than the related existing methods.

Binocular visual characteristics based fragile watermarking scheme for tamper detection in stereoscopic images

In the past decade, various fragile digital watermarking techniques have been proposed for monoscopic image authentication and temper detection. In this paper, a novel binocular visual characteristics based pixel-wise fragile watermarking scheme for stereoscopic image authentication and tamper detection is proposed. The scheme consists of two processes: embedding of the stereoscopic image authentication message and tamper detection. In the watermark embedding process, the binocular just noticeable difference (BJND) model is used for guiding watermark embedding, which is convenient for achieving a balanced relationship between watermarking capacity and visual transparency. In the tamper detection process, a probability theory and an algorithm of binocular disparity are employed to improve previously obtained detection results and to enhance authentication accuracy. Moreover, to improve the security of the proposed scheme, MD5 hash function and chaotic map are used. Experimental results reveal that the proposed scheme is not only secure, but also achieves superior tamper detection for different attacks.

Sharpness Enhancement of Stereo Images Using Binocular Just-Noticeable Difference

In this paper, we propose a new sharpness enhancement algorithm for stereo images. Although the stereo image and its applications are becoming increasingly prevalent, there has been very limited research on specialized image enhancement solutions for stereo images. Recently, a binocular just-noticeable-difference (BJND) model that describes the sensitivity of the human visual system to luminance changes in stereo images has been presented. We introduce a novel application of the BJND model for the sharpness enhancement of stereo images. To this end, an overenhancement problem in the sharpness enhancement of stereo images is newly addressed, and an efficient solution for reducing the overenhancement is proposed. The solution is found within an optimization framework with additional constraint terms to suppress the unnecessary increase in luminance values. In addition, the reliability of the BJND model is taken into account by estimating the accuracy of stereo matching. Experimental results demonstrate that the proposed algorithm can provide sharpness-enhanced stereo images without producing excessive distortion.

Binocular Just-Noticeable-Difference Model for Stereoscopic Images

Conventional 2-D Just-Noticeable-Difference (JND) models measure the perceptible distortion of visual signal based on monocular vision properties by presenting a single image for both eyes. However, they are not applicable for stereoscopic displays in which a pair of stereoscopic images is presented to a viewer's left and right eyes, respectively. Some unique binocular vision properties, e.g., binocular combination and rivalry, need to be considered in the development of a JND model for stereoscopic images. In this letter, we propose a binocular JND (BJND) model based on psychophysical experiments which are conducted to model the basic binocular vision properties in response to asymmetric noises in a pair of stereoscopic images. The first experiment exploits the joint visibility thresholds according to the luminance masking effect and the binocular combination of noises. The second experiment examines the reduction of visual sensitivity in binocular vision due to the contrast masking effect. Based on these experiments, the developed BJND model measures the perceptible distortion of binocular vision for stereoscopic images. Subjective evaluations on stereoscopic images validate of the proposed BJND model.