Human Visual Characteristics Research Articles

Multiple Description Video Coding Based on Human Visual System Characteristics

In this paper, a novel multiple description video coding scheme is proposed based on the characteristics of the human visual system (HVS). Due to the underlying spatial-temporal masking properties, human eyes cannot sense any changes below the just noticeable difference (JND) threshold. Therefore, at an encoder, only the visual information that cannot be predicted well within the JND tolerance needs to be encoded as redundant information, which leads to more effective redundancy allocation according to the HVS characteristics. Compared with the relevant existing schemes, the experimental results exhibit better performance of the proposed scheme at same bit rates, in terms of perceptual evaluation and subjective viewing.

이미지 화질개선을 위한 Weber-Fechner 법칙을 적용한 가중 히스토그램 균등화 기법

저화질 이미지의 화질 개선에는 전통적으로 히스토그램균등화 기법이 사용되어 왔다. 히스토그램균등화 기법은 입력 이미지의 누적밀도함수를 변환함수로 사용하는 기법으로 이는 이론상 최대의 엔트로피를 가지지만 주관적 화질 측면에서는 백화현상이 나타나는 문제점이 있다. 본 논문에서는 히스토그램균등화 기법 기반의 가중 히스토그램 균등화 기법을 제안한다. 이는 인간의 시각특성을 반영한 Weber-Fechner 법칙을 사용하며 입력영상에 독립적인 변환함수를 제공하는 여러 이미지 화질 개선 기법들이 가지는 문제점을 해결하기 위해서 동적영역 재조정 과정을 포함한다. 최종적으로 재조정된 동적영역 범위 내에서 Weber-Fechner 법칙을 적용한 변환함수와 히스토그램균등화 기법을 통해 얻어진 변환함수간의 가중 평균을 통하여 변환함수를 생성한다. 실험결과 제안하는 알고리즘은 주관적 화질 측면에서 대비비를 효과적으로 향상시키는 것을 보여주며, 엔트로피 또한 비교에 사용된 여러 이전의 방법들과 비교하여 유사하거나 높은 값을 가지는 것을 볼 수 있었다. A histogram equalization method have been used traditionally for the image enhancement of low quality images. This uses the transformation function, which is a cumulative density function of an input image, and it has mathematically maximum entropy. This method, however, may yield whitening artifacts. This paper proposes the weighted histogram equalization method based on histogram equalization. It has Weber-Fechner's law for a human's vision characteristics, and a dynamic range modification to solve the problem of some methods, which yield a transformation function, regardless of the input image. Finally, the proposed transformation function was calculated using the weighted average of Weber-Fechner and the histogram equalization transformation functions in a modified dynamic range. The simulation results showed that the proposed algorithm effectively enhances the contrast in terms of the subjective quality. In addition, the proposed method has similar or higher entropy than the other conventional approaches.

A Fuzzy Clustering Algorithm of Automatic Classification Based on EnFCM

Fuzzy c-means (FCM) algorithm is an unsupervised clustering algorithm for image segmentation, and has been widely applied because the segmentation results are consistent with human visual characteristics. Enhanced fuzzy c-means clustering (EnFCM) algorithm is the improved FCM algorithm, which reduces the computational complexity. But, both FCM algorithm and EnFCM algorithm, clustering number still need to be manually determined. This paper, in order to realize the automation degree of algorithm, presents an improved algorithm. It first analyzes the histogram, then automatically determines the clustering number and peak value of each class through use of the peak point detection technology, finally segments image by using EnFCM algorithm. Experiments show that this method is a kind of faster fuzzy clustering algorithm with automatic classification ability for image segmentation.

Adaptive Pixel Crosstalk Compensation for CMOS Image Sensor

A new algorithm for removing pixel crosstalk in CMOS image sensors with Bayer color filter array was proposed. The algorithm can estimate low/high frequency image and provide adaptive threshold based on the human visual characteristics. By simulation experimentation on Macbeth color checker, resolution checker and real image, the experimental results revealed that this algorithm can remove the pixel crosstalk effectively.

A Video Characteristics Watermarking Algorithm Based on Bees Evolutional Computation

Digital video watermarking is an efficient way of copyright protection. A robust watermarking was proposed that ccording to the video features using the evolutional method of video image classification processing, suitable for video performance and human visual characteristics of video watermarking, and put forward a kind of using Bees evolutional algorithm for video image classification processing of video watermarking algorithm. All the key frame numbers are converts to a binary matrix after data transformation. At last the binary image is produced. From this, the watermarking image reflects the video’s features, so it is effective to resist the watermark copy attack.

Suggestion for a Visual Dynamics Analysis Model Using a Natural Movement Model

The purpose of this study is to suggest a visual dynamics analysis (VDA) model that comprises a visibility analysis model integrated with a natural movement model. The theory of ′natural movement′ from the background of Gibson′s ecological theory of perception and Hillier′s research could help to understand the relations between visual perception and movement. In a previous study, the natural movement model called the EVA (Exosomatic Visual Architecture) model was proposed by Turner and Penn with the concept of visual dynamics. However, the EVA model could not give any information about agent visual experience, but only a pattern of vision-driven agent movement. This is because spatial visual information already exists outside of the agent. Most of all, the model limits any visual dynamics analysis because the agents′ movement patterns are irregular at a micro level. Therefore, in this study, a new natural movement model rule that shows good movement patterns at the micro scale is discussed, and visual indexes considering human visual characteristics and dynamic analysis are proposed. These are integrated with the VDA model and programmed with NetLogo. The analysis of test spaces by the VDA model and the meaning of the model are discussed.

Human Visual Characteristics Inspired Adaptive Image Quantization Method

Human Visual Characteristics Inspired Adaptive Image Quantization Method

Achieving Secrecy in Visual Secret Sharing Scheme Using Encrypted Images

Visual cryptography (VC), an emerging cryptography technology, uses the characteristics of human vision to decrypt encrypted images. Cryptography is the study of mathematical techniques related to aspects of information security such as confidentiality, data security, entity authentication, and data origin authentication, but it is not the only means of providing information security, rather one of the techniques. VC is a new technique that provides information security which uses simple algorithms unlike the complex, computationally intensive algorithms used in other techniques like traditional cryptography. Previous methods in the literature show good results for black and white VC schemes, however, they are not sufficient to be applied directly to shares due to different structures. Some methods for color VC are not satisfactory in terms of producing either meaningless shares or meaningful shares with low visual quality, leading to suspicion of encryption. Visual secret sharing encrypts a secret message into halftone image shares. This article introduces the concept of visual information pixel (VIP) synchronization and error diffusion to attain a visual cryptography encryption method that produces meaningful shares with high visual quality. VIP synchronization retains the positions of pixels carrying visual information of original images throughout the channels and error diffusion generates shares pleasant to human eyes. Comparisons with previous approaches show the superior performance of the new method.

A New Medical Image Enhancement Method Based on Human Visual Characteristics

Studying of image enhancement shows that the quality of image heavily relies on human visual system. In this paper, we apply this fact to design a new image enhancement method for medical images that improves the detail regions. First, the eye region of interest (ROI) is segmented; then the Un-sharp Masking (USM) is used to enhance the detail regions. Experiments show that the proposed method can effectively improve the accuracy of medical image enhancement and has a significant effect.

An Improved Image Contrast Assessment Method

Contrast is an important factor affecting the image quality. In order to overcome the problems of local band-limited contrast, a novel image contrast assessment method based on the property of HVS is proposed. Firstly, the image by low-pass filter is performed fast wavelet decomposition. Secondly, all levels of band-pass filtered image and its corresponding low-pass filtered image are obtained by processing wavelet coefficients. Thirdly, local band-limited contrast is calculated, and the local band-limited contrast entropy is calculated according to the definition of entropy, Finally, the contrast entropy of image is obtained by averaging the local band-limited contrast entropy weighed using CSF coefficient. The experiment results show that the best contrast image can be accurately identified in the sequence images obtained by adjusting the exposure time and stretching gray respectively, the assessment results accord with human visual characteristics and make up the lack of local band-limited contrast. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.3719 Full Text: PDF

The importance of image quality: In the eyes of the beholder?

The importance of image quality: In the eyes of the beholder?

A novel macroblock level rate control method for stereo video coding.

To compress stereo video effectively, this paper proposes a novel macroblock (MB) level rate control method based on binocular perception. A binocular just-notification difference (BJND) model based on the parallax matching is first used to describe binocular perception. Then, the proposed rate control method is performed in stereo video coding with four levels, namely, view level, group-of-pictures (GOP) level, frame level, and MB level. In the view level, different proportions of bitrates are allocated for the left and right views of stereo video according to the prestatistical rate allocation proportion. In the GOP level, the total number of bitrates allocated to each GOP is computed and the initial quantization parameter of each GOP is set. In the frame level, the target bits allocated to each frame are computed. In the MB level, visual perception factor, which is measured by the BJND value of MB, is used to adjust the MB level bit allocation, so that the rate control results in line with the human visual characteristics. Experimental results show that the proposed method can control the bitrate more accurately and get better subjective quality of stereo video, compared with other methods.

Efficient wavelet‐based perceptual watermark masking for robust fingerprint image watermarking

In this study, a robust wavelet-based fingerprint image watermarking scheme using an efficient just perceptual weighting (JPW) model has been proposed. The JPW model exploits three human visual system characteristics, namely: spatial frequency sensitivity, local brightness masking and texture masking, to compute a weight for each wavelet coefficient, which is then used to control the amplitude of the inserted watermark. The idea is motivated by the fact that fingerprint images perceptually differ from natural images and a JPW model adapted to such images would further enhance the robustness of the watermarking scheme. Experimental results show that the proposed model significantly improves the performance of the conventional watermarking technique in terms of robustness while maintaining the same imperceptibility of the watermark. Finally, the proposed technique has shown a clear superiority over a number of related state-of-the-art masking techniques.

DWT-Based Multisource Image Fusion Using Spatial Frequency and Simplified Pulse Coupled Neural Network

In this paper, we present a new discrete wavelet transform (DWT)-based multisource image fusion algorithm using spatial frequency and a simplified pulse coupled neural network model named spiking cortical model (SCM). The multiscale decomposition and multi-resolution representation characteristics of DWT are associated with global coupling and pulse synchronization features of SCM. Firstly, source images are decomposed into low frequency sub-bands and high frequency sub-bands by DWT. Secondly, considering human visual system characteristics, two different fusion rules are used to fuse low and high frequency sub-bands respectively. Maximum selection rule (MSR) is used to fuse low frequency coefficients. As to high frequency subband coefficients, spatial frequency (SF) is calculated and then imputed into SCM to motivate neural network rather than inputting coefficients value directly, and then the time matrix of SCM is set as criteria to select coefficients of high frequency subband. The effectiveness of the proposed algorithm is achieved by the comparison with existing fusion methods.

基于人眼感知特性的彩色图像水印技术研究

Combined with contrast sensitivity characteristics of human vision system and image feature in transform domain,a new method is proposed for calculating the human perception error threshold of image.Combining the calculated threshold,a new color image watermarking scheme is brought forward,and the attack test and simulation experiments are carried out.The results show that,watermark information extracted from attacked images embedded watermark is still visible,the normalized coefficient values after compression attack with the compression quality factor 50% still can reach more than 0.7371,which shows that watermarking scheme proposed has good transparency and high robust.And in the watermark embedding and extraction process,the original color image is transform into the device-independent CIELAB color space in order to improve the robustness of the watermark.Synthesis results show that the proposed watermarking scheme is an effective watermark embedding and extraction scheme.

Just Noticeable Difference Based Fast Coding Unit Partition in HEVC Intra Coding

As an ongoing video compression standard, High Efficiency Video Coding (HEVC) has achieved better rate distortion performance than H.264, but it also leads to enormous encoding complexity. In this paper, we propose a novel fast coding unit partition algorithm in the intra prediction of HEVC. Firstly, instead of the time-consuming rate distortion optimization for coding mode decision, just-noticeable-difference (JND) values can be exploited to partition the coding unit according to human visual system characteristics. Furthermore, coding bits in HEVC can also be considered as assisted information to refine the partition results. Compared with HEVC test model HM10.1, the experimental results show that the fast intra mode decision algorithm provides over 28% encoding time saving on average with comparable rate distortion performance.

Cognitive Analysis of Product Form Elements

Perception is the most basic form of cognition thinking activity. Consumers' perception image to the product form is based on human's visual perception characteristics, which can be summarized as the overall organization, the constant memory, the simple regulating and the identifiable discrimination according to the Gestalt principle. Firstly, the study analyzes the characteristics of consumers' perception image to the product form. Secondly, the evaluation model that is used to simulate consumers' image perceived behavior is build using gray correlation analysis and fuzzy neural network. Finally, the practice example of car side profile design is taken, and the results show that the method is helpful to product form image intelligent design. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3696 Full Text: PDF

Research on the Color-Difference Evaluation of Food Package Printing Matter Based on the Human Visual Characteristics

In order to improve for the inadequacy of traditional evaluation methods, this paper researched a kind of color-difference evaluation method for the food package printing matter based on human visual characteristics. Research method is as follow: the reference image and the target image were transformed to the opposite color space, and were dealt with the human eye Contrast Sensitive Function (CSF) spatial filter. The modified three opposite color component of images were transformed into the CIE XYZ space image after CSF spatial filtering processed; finally the image was transformed to the CIELAB space image. The color-difference of the target image was calculated according to color-difference formula. The color-difference result of image is the calculation result with human visual characteristics filtering processed; it combines subjective evaluation with objective evaluation. The research could provide practical and effective evaluation method for the field of food package printing matter.

Research of Multi-sensor Images Based on Color Fusion Methods

With the development of image sensor technology, multi-sensor image fusion technology emerged and was widely used in the field of military surveillance, medical diagnosis, remote sensing, intelligent robot and so on. However, the current image fusion technology mainly focuses on the research of gray images, the color image fusion is rarely. Because color image contains more information compared with gray image, the research on color image fusion technology is becoming more and more urgent. In this paper, the realization of several typical color image fusion algorithms were discussed, the principle and their respective advantages and disadvantages were analyzed. Secondly, according to the different characteristics of visible image and infrared image, this paper proposes a color image fusion algorithm based on Curve let transform, this algorithm will combine visible image, infrared image with its negative respectively fusion, color mapping rules are in couple with the human visual characteristics. Experiments show that color fusion images obtained are richer in color, they contains more details and recognize easily.

Image Quality Assessment Based on Contourlet and Gradient Structural Similarity

To compensate for the defect of inaccurate evaluation of fuzzy distortion by the SSIM original algorithm, following the existing gradirent structure similarity algorithm, considering the multi-resolution and multidirectional characteristics of the Contourlet transform, an image quality assessment algorithm based on Contourlet and Gradient Structural Similarity (CGSSIM) was proposed. The experimental results show that the CGSSIM algorithm is more suitable for the human visual system characteristics than the PSNR and SSIM algorithm and it can get better image quality evaluation results.