Characteristics Of Human Visual System 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.

The HIS_MSR Algorithm for Foggy Image Enhancement

In this paper, on the basis of the classical Retinex enhancement algorithm, we propose a MSR (Multi-Scale Retinex) algorithm based on HSI color space for foggy images enhancement. This algorithm can self-adaptively choose the Scale of the filter function, and it can automatically merge the enhancement effect of the multi-scales. The algorithm can highlight the detail characteristics of target while maintaining the background smoothly. In order to improve the brightness and contrast of the image, we use the Weber's law and the characteristics of human visual system to select the scale parameters, which can better realized the foggy images enhancement.

A Novel Method of Image Quality Assessment

Image quality assessment (IQA) is one of the hot research areas in the field of image processing. For the reason that human being is the final receiver of the image, the image quality assessment should match the characteristics of human visual system. In this paper, we propose a novel method of image quality assessment which uses the visual selective attention of human visual system. For an image of a certain category, our method firstly detects the object in it and then calculate the saliency of the object. Lastly we use the combination of the detector’s score and the saliency as the image quality assessment. Experiments on some images of Pascal VOC dataset and INRIA dataset show that our method does well in image quality assessment.

혼합 해상도 양안식 영상에 대한 주관적 화질평가를 통한 고정 및 이동 융합형 3DTV 서비스의 품질 검증

양안식 3D 비디오의 효율적인 압축을 위해 다양한 기술들이 연구되었으며, 그 중 좌우영상으로 상이한 해상도의 영상을 사용하는 방식은 인간의 시각 시스템이 혼합 해상도의 양안식 영상을 높은 쪽 영상의 품질에 가깝게 인지하는 특성을 이용한 대표적인 비트레이트 절감 방식이다. 그러나 혼합 해상도 양안식 영상에서 좌우안 영상 해상도 차이가 일정 범위 이상일 경우 3D 영상의 품질 저하가 발생한다. 이에 다양한 연구에서 혼합 해상도 양안식 영상의 좌우해상도 차이에 따른 화질 저하 정도를 측정하려는 시도가 이루어졌으나, 기존 연구에서는 시청거리를 고려하지 않고 단순 좌우 영상의 크기만을 고려하여 실험하였으며 이에 각 연구별로 상이한 실험 결과가 도출되었다. 본 연구에서는 인간의 시각 시스템을 고려하여 최적 시청거리를 계산하고, 이에 기반하여 좌우영상 비율 별 주관적 화질평가를 수행하였다. 또한 실험 결과 분석을 통해 혼합 해상도 영상 기반의 고정 및 이동 융합형 3DTV 기술이 고품질 3D 서비스를 제공할 수 있음을 보인다. Various techniques have been developed for efficient compression of stereoscopic 3D videos. Mixed-resolution based approach is one representative bit-rate saving method based on the characteristics of human visual system that the mixed-resolution stereoscopic videos are perceived close to the higher resolution. However, when the difference between the left and right image resolutions is bigger than a certain threshold level, it causes the perceived quality degradation of the 3D images. Subsequently, several researches tried to find the correlation between the difference in resolution and the level of the perceived quality degradation, but they conducted the experiments just considering the difference in resolution without considering the viewing distances, so thereby different results were retrieved from test to test. In this work, we calculated the optimal viewing distance based on the human visual system, and conducted the subjective tests with the calculated viewing distance. With the results, we demonstrate that the fixed and mobile hybrid 3DTV, which is based on mixed-resolution stereoscopic images, can provide the high quality 3D services.

A Survey of Watermarking Algorithm Based on Edge Detection

This paper introduces the application of edge detection methods in digital watermarking. Based on the characteristics of Human Visual System, in some watermarking schemes copyright information is embedded into edges or textured areas using edge detection methods. This survey covers the methods of edge detection used in watermarking system. A general model of watermarking embedding device based on edge detection is proposed. The research of determination of watermarking embedding position and strength in those schemes has been done. We give a detail analysis of those based on spatial domain and those based on transform domain. Finally, we point out the next steps on which we can keep going up, hoping for a better balance between watermarking robustness and perceptibility.

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.

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.

Perceptual quality assessment of SAR image compression

Image quality assessment (IQA) is of fundamental importance for image compression applications. Traditional IQA measures for synthetic aperture radar (SAR) image compression do not consider the properties of the human visual system (HVS). Since human beings are still the final users in most SAR image applications, the objective evaluation coordinate to human perception is the most acceptable and practical IQA method. In this article, we carried out an extensive subjective quality assessment experiment in which four different SAR image compression techniques and a total of 300 test images were estimated by 100 human observers. The test result provides a straightforward comparison of these four compression methods. Then the performances of seven classical objective IQA algorithms were validated based on obtained subjective evaluations. Moreover, we propose a novel objective approach by taking into account the HVS and SAR image characteristics. Experimental results demonstrate that the proposed metric correlates well with human perception for SAR image compression.

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.

A New Image Fusion Method Based on Improved PCNN and Multiscale Decomposition

A new image fusion algorithm based on nonsubsampled contourlet transform and spiking cortical model is proposed in this paper. Considering the human visual system characteristics, two different fusion rules are used to fuse the low and high frequency sub-bands of nonsubsampled contourlet transform respectively. A new maximum selection rule is defined to fuse low frequency coefficients. Spatial frequency is used for the fusion rule of high frequency coefficients. Experimental results demonstrate the effectiveness of the proposed fusion method.

New no-reference image quality assessment method based on decomposition of gradient similarity

At present,the existing general no-reference image quality assessment methods are used for special purpose.Furthermore,the distortion type of the image is unknown in practical application.Most of no-reference image quality assessment methods are based on the geometrical description feature of image,but this type of method relies on the boundary of image too much.To solve this problem,a no-reference image quality assessment method was proposed based on the gradient similarity decomposition of image,which was named Decomposition of Gradient Similarity(DGS).The proposed method extracted the gradient feature and decomposed the correlation of gradient as the main structure information of the image.The experimental results show that the proposed DGS model is better than Peak Signal-to-Noise Ratio(PSNR)(or Mean Square Error(MSE)) model,which is more sensitive to Human Visual System(HVS) characteristics and more consistent to the subjective evaluation value.

Perceptual Visual Signal Compression and Transmission

One- and two-way communication with digital compressed visual signals is now an integral part of the daily life of millions. Such commonplace use has been realized by decades of advances in visual signal compression. The design of effective, efficient compression and transmission strategies for visual signals may benefit from proper incorporation of human visual system (HVS) characteristics. This paper overviews psychophysics and engineering associated with the communication of visual signals. It presents a short history of advances in perceptual visual signal compression, and describes perceptual models and how they are embedded into systems for compression and transmission, both with and without current compression standards.

Bi-orthogonal Wavelet based Adaptive Image Watermarking using Human Visual System and Fuzzy Inference System

Digital age today has shown increasing reliance on digital media and rapid distribution of digital contents. The issues of piracy and copyright are of immense concern in this exponentially increasing digital revolution. In this paper, authors propose use of bi-orthogonal wavelet transform as an effective tool to address copyright infringement issues. A synergistic combination of bi-orthogonal wavelet transform, human visual system characteristics and fuzzy inference system is arrived at to develop a novel adaptive image watermarking scheme. Logo watermark is used to prove ownership of digital property. A hierarchical data structure is generated using bi-orthogonal wavelet decomposition to represent an image. The reconstruction of image is achieved by computing inverse wavelet transform of modified wavelet coefficients. The unique feature of the proposed scheme is that fuzzy inference system is used to effectively establish relation between different HVS characteristics and adaptively adjusting embedding strength of watermark. General Terms Image Watermarking

Multimodal Medical Image Fusion Framework Based on Simplified PCNN in Nonsubsampled Contourlet Transform Domain

In this paper, we present a new medical image fusion algorithm based on nonsubsampled contourlet transform (NSCT) and spiking cortical model (SCM). The flexible multi-resolution, anisotropy, and directional expansion characteristics of NSCT are associated with global coupling and pulse synchronization features of SCM. Considering the human visual system characteristics, two different fusion rules are used to fuse the low and high frequency sub-bands respectively. Firstly, maximum selection rule (MSR) is used to fuse low frequency coefficients. Secondly, spatial frequency (SF) is applied to motivate SCM network rather than using 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.

A Digital Watermarking Algorithm for Color Image Based on DWT

An efficient digital watermark embedding algorithm for color image was presented in this paper, which is based on the discrete wavelet transform (DWT) and the spectral characteristics of human vision system. Firstly, three color separations was performed for color image, and color components of color image were transformed by DWT. Secondly, the embedding position of the watermark was confirmed by comparing the energy value of the low frequency sub-band in the transformed blue component and green component. Thirdly, the watermark was made Arnold Transform for encryption and was embedded in the color component with a larger power. Finally, this paper made the simulation experiments to evaluate the performance of the watermark. The simulation results showed that the embedded watermark had good invisibility and robustness for the common image processing, such as filtering, noise, especially compression and cropping. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2681 Full Text: PDF

H.264/Advanced Video Control Perceptual Optimization Coding Based on JND-Directed Coefficient Suppression

The field of video coding has been exploring the compact representation of video data, where perceptual redundancies in addition to signal redundancies are removed for higher compression. Many research efforts have been dedicated to modeling the human visual system's characteristics. The resulting models have been integrated into video coding frameworks in different ways. Among them, coding enhancements with the just noticeable distortion (JND) model have drawn much attention in recent years due to its significant gains. A common application of the JND model is the adjustment of quantization by a multiplying factor corresponding to the JND threshold. In this paper, we propose an alternative perceptual video coding method to improve upon the current H.264/advanced video control (AVC) framework based on an independent JND-directed suppression tool. This new tool is capable of finely tuning the quantization using a JND-normalized error model. To make full use of this new rate distortion adjustment component the Lagrange multiplier for rate distortion optimization is derived in terms of the equivalent distortion. Because the H.264/AVC integer discrete cosine transform (DCT) is different from classic DCT, on which state-of-the-art JND models are computed, we analytically derive a JND mapping formula between the integer DCT domain and the classic DCT domain which permits us to reuse the JND models in a more natural way. In addition, the JND threshold can be refined by adopting a saliency algorithm in the coding framework and we reduce the complexity of the JND computation by reusing the motion estimation of the encoder. Another benefit of the proposed scheme is that it remains fully compliant with the existing H.264/AVC standard. Subjective experimental results show that significant bit saving can be obtained using our method while maintaining a similar visual quality to the traditional H.264/AVC coded video.

Video quality evaluation based on temporal feature of HVS

For the videos that have fast changing motion scenes,the existing simulation of the human visual system is less effective in quality assessment.Due to the bandpass and masking features of the subjective testers,the objective evaluation model ignores the two temporal features of the Human Visual System(HVS),which leads to the deviation between the subjective and objective evaluation results.To improve the evluation performance of the rapidly changing videos,the visual threshold was determined by using the statistic learning method and the filtering of the HVS was built up.The masking of human eyes was emulated through a new attenuation-weight function.The experimental results demonstrate that the proposed method obtained the best performance when the lost packets rates was lower than 5 percent compared with the Peak Signal-to-Noise Ratio(PSNR) method,the constant-weight evaluation model and the rule evaluation model.With the filter function of the bandpass model,it improved the execution efficiency.In brief,the proposed method not only improves the evaluation performance but also reduces the computational complexity.

A temporal memory-based ordered dithering using recurrent neural network

Ordered dither is a popular digital halftoning technique used for bi-level displays. Despite its popularity, this technique results in two major problems e.g. objectionable periodic patterns and false contouring. This paper presents a temporal memory-based approach for ordered dithering. Digital halftoning has been considered as an optimisation problem. The temporal memory of recurrent neural network (RNN) has been employed to generate optimised halftone patterns. The temporal memory of RNN is used to retain association between pixels under processing and pixels already processed for generating the screen matrix. The human visual system (HVS) characteristics have been involved in cost function formulation. The proposed framework has been investigated with two most commonly practiced architectures of RNN, namely, Elman RNN and Jordan RNN. The results obtained using both the architectures have been presented pictorially. The results have also been evaluated against quality evaluation metrics such as peak signal-to-noise ratio (PSNR), universal quality index (UQI) and structural similarity index measure (SSIM). The results show that the proposed method may be potentially useful in the field of digital halftoning.

Adaptive, scalable and robust watermarking for wavelet-based progressive image transmission

Image watermarking refers to the process of embedding an authentication message, called watermark, into the host image to uniquely identify the ownership. In this paper, an adaptive, scalable, blind and robust wavelet-based watermarking approach is proposed. The proposed approach enables scalable watermark detection and provides robustness against progressive wavelet image compression. A multiresolution decomposition of the binary watermark is inserted into the selected coefficients of the wavelet-decomposed image that represent the high activity regions of the image. The watermark insertion is started from the lowest frequency sub-band of the decomposed image and each decomposed watermark sub-band is inserted into its counterpart sub-band of the decomposed image. In the lowest frequency sub-band, coefficients with maximum local variance and in the higher frequency sub-bands, coefficients with maximum magnitude are selected. The watermarked test images are transparent according to the human vision system characteristics and do not show any perceptual degradation. The experimental results very efficiently prove the robustness of the approach against progressive wavelet image coding even at very low bit rates. The watermark extraction process is completely blind and multiple spatial resolutions of the watermark are progressively detectable from the compressed watermarked image. This approach is a suitable candidate for providing efficient authentication for progressive image transmission applications especially over heterogeneous networks, such as the Internet.