Efficient no-reference metric for sharpness mismatch artifact between stereoscopic views

This paper analyzes sharpness mismatch between stereoscopic views. Sharpness mismatch is a special binocular mismatch and can occur through e.g. focus mismatch between stereoscopic cameras, errors in post-processing or asymmetric coding for low-bandwidth transmission, where one view is subsampled or transmitted at a much lower rate. Although blurred edges in one view can be suppressed by the corresponding sharper edges in the other view according to the binocular suppression phenomenon, sharpness mismatch can still be perceived and cause eye strain for viewers. Subjective studies were carried out with a test video dataset, in which the stereoscopic views are asymmetrically blurred by Gaussian low-pass filters since defocus-based effects of lens aberrations can be modeled as Gaussian blur. Also, an efficient novel automatic no-reference approach to measure the probability of sharpness mismatch is presented in this paper. The sharpness mismatch score is estimated by measuring width deviations of edge pairs in each “edge-significant” depth plane based on depth edges in both views. The probability of sharpness mismatch (PSM) is then calculated considering the perceptibility of edge width deviations considering absolute depth at which the edges occur. This PSM metric is evaluated using the test video dataset and blurriness dataset of LIVE 3D Phase II database. The experimental results show that the proposed metric outperforms the state-of-the-art stereo 3D quality metrics on analyzing sharpness mismatch between stereoscopic views.     

Adaptive Block-size Transform based Just-Noticeable Difference model for images/videos

In this paper, we propose a novel Adaptive Block-size Transform (ABT) based Just-Noticeable Difference (JND) model for images/videos. Extension from 8×8 Discrete Cosine Transform (DCT) based JND model to 16×16 DCT based JND is firstly performed by considering both the spatial and temporal Human Visual System (HVS) properties. For still images or INTRA video frames, a new spatial selection strategy based on the Spatial Content Similarity (SCS) between a macroblock and its sub-blocks is proposed to determine the transform size to be employed to generate the JND map. For the INTER video frames, a temporal selection strategy based on the Motion Characteristic Similarity (MCS) between a macroblock and its sub-blocks is presented to decide the transform size for the JND. Compared with other JND models, our proposed scheme can tolerate more distortions while preserving better perceptual quality. In order to demonstrate the efficiency of the ABT-based JND in modeling the HVS properties, a simple visual quality metric is designed by considering the ABT-based JND masking properties. Evaluating on the image and video subjective databases, the proposed metric delivers a performance comparable to the state-of-the-art metrics. It confirms that the ABT-based JND consists well with the HVS. The proposed quality metric also is applied on ABT-based H.264/Advanced Video Coding (AVC) for the perceptual video coding. The experimental results demonstrate that the proposed method can deliver video sequences with higher visual quality at the same bit-rates.     

Image quality assessment based on edge preservation

Objective image/video quality metrics which accurately represent the subjective quality of processed images are of paramount importance for the design and assessment of an image compression and transmission system. In some scenarios, it is also important to evaluate the quality of the received image with minimal reference to the transmitted one. For instance, for closed-loop optimization of a transmission system, the image quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image – prior to compression and transmission – is not usually available at the receiver side, and it is important to rely at the receiver side on an objective quality metric that does not need reference or needs minimal reference to the original image. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art reduced reference metric.     

MDVQM: A novel multidimensional no-reference video quality metric for video transcoding

In this paper, we study the impact of quantization, frame dropping and spatial down-sampling on the perceived quality of compressed video streams. Based on the analysis of quality ratings obtained from extensive subjective tests, we propose a no-reference metric (named MDVQM) for video quality estimation in the presence of both spatial and temporal quality impairments. The proposed metric is based on the per-pixel bitrate of the encoded stream and selected spatial and temporal activity measures extracted from the video content. All the values required to compute the proposed video quality metric can be obtained without using the original reference video which makes the metric for instance useful for making transcoding decisions in a wireless video transmission scenario. Different from comparable metrics in the literature, we have also considered the case when both frame rate and frame size are changed simultaneously. The validation results show that the proposed metric provides more accurate estimation of the video quality than the state of the art metrics.     

图像质量快速盲检测及其在视觉系统中的应用

提出了一种新的图像质量快速盲检测方法,以锐利度作为图像质量准则,而锐利度可以归结为图像的模糊特性,并由边缘信息估计得到。为了提高系统效率,采用边缘对比度的估计方法,并以边缘对比度作为边缘锐利度的判据准则。分析了邻近边缘的相互影响,因而只需从有限的边缘点来提取出合理的线扩展函数(LSF),进而得到模糊参数。在LSF计算中,采用一种新的插值计算方法。实验结果表明:当图像不是特别模糊时,测量结果是准确的。     

图像边缘信息的谱分析及其在变换编码中的应用

本文对边缘信息的ＤＣＴ频谱特征做了分析。通过理论推导及事例分析证得含平直单边缘图像子块，其ＤＣＴ变换系数能量并不总是集中分布在与其空间边缘走向垂直的方向上。将研究结果应用于变换编码的分类环节，提出一种按边缘信息的频谱特性分类的方法。实验结果表明了该方法的有效性。     

Non-reference assessment of sharpness in blur/noise degraded images

Image sharpness perception is not only affected by blur but also by noise. Noise effect on perceived image sharpness is a puzzling problem since image sharpness may increase, up to a certain amount of noise, on even regions when noise is added to an image. In this paper, we propose a NR perceived sharpness metric GSVD (Gradient Singular Value Decomposition), that shows to be effective in correlating with subjective quality evaluation of images affected by either blur or noise. This metric (i) requires no training on human image quality ratings, (ii) provides comparable performance with full reference (FR) peak signal to noise ratio (PSNR) and multiscale structural similarity (MSSIM), and (iii) performs better than most of the state-of-the-art NR sharpness metrics when assessing quality in blurry image sets and noisy image sets jointly.     

Adaptive enhancement of compressed SAR images

Synthetic aperture radar (SAR) imagery is an important global all-weather surveillance and mapping satellite imagery system. As space-borne systems have a limited storage capacity, it is imperative to heavily compress SAR images, possible with lossy compression schemes. As a result, SAR images need to be enhanced in earth stations. The work reported in this paper aims to address the issue of compression artefact removal of SAR images in an adaptive manner. The SAR images, compressed using the JPEG utility at significantly low bit rates, are enhanced by adaptively removing coding artefacts and speckle noise. As edges carry significant information in satellite imagery, a significant edge image is used for edge enhancement with selective removal of noisy edges. Further, an image sharpness metric is proposed in this work to serve as an objective no-reference metric for measuring the sharpness of SAR images.     

基于梯度结构相似度的无参考模糊图像质量评价

在对模糊图像边缘膨胀后进行边缘膨胀块提取的 基础上,提出一种基于梯 度结构相似度(GSIM)的无参考模糊图像质量评价方法(NRGSIM)。首先,将原始模糊图像经过低 通滤波生成再模糊图像;之后,将原始模糊图像生成的边缘膨胀图像进行8×8分块,并将子块 划分为边缘膨胀块和平滑块;然后,计算原始模糊图像和再模糊图像中所有对应到边缘膨胀 图 中边缘膨胀块的相应子块的GSIM;最后,平均得到整幅图像的模糊值。在4个数据 库上实验结果表明,本文方法评价结果合理、稳定,更加符合人类视觉特性,与主观评分有 较好的一致性,而且计算简单,取得了很好的评价效果,LIVE2数据库上的SROCC指标达到0.964。     

Non-stationary content-adaptive projector resolution enhancement

Digital imagery and video can have content at higher resolutions than can be projected by most data projectors, which has led to a variety of techniques to improve the high-resolution perception from lower-resolution displays. However, the downsampling procedures frequently used to fit an original image or video of high-resolution into a lower-resolution projector cause a frustrating loss of fine structures in the projected imagery. Since the human visual system is more sensitive to certain image phenomena, such as text and edges, an optimal approach to preserving fine structures should further sharpen such displayed content. On the other hand, the human visual system is also very sensitive to aliasing effects in motion, such that over-sharpening can lead to significant motion artifacts.In this paper, a new non-stationary content-adaptive resolution enhancement scheme is proposed. Our main objective in this study is to reduce the severity of artifacts due to the image enhancement processes. To achieve this goal, distribution-based text detection and hypothesis-testing-based motion detection methods are developed. Three spatial kernels, each constructed using a new band-limited Wiener deconvolution filter, are used to enhance a given image with different sharpening strengths, where the differently enhanced images are combined using a weighted non-stationary filter. For evaluation, a new visual projection assessment (VPA) dataset along with new metrics for quantifying motion artifacts are introduced. Experimental results show that the proposed non-stationary content-adaptive resolution enhancement scheme offers improved visual quality over the state-of-the-art while offering a reasonable balance between high text sharpness and reduced motion artifacts.     

A no-reference sharpness metric based on the notion of relative blur for Gaussian blurred image

This work presents a no-reference sharpness metric for Gaussian blurred image. The metric is based on the notion of relative blur. The key concept is that the judgement on the sharpness closely relates to the degree of convenience for recognizing image objects on a certain scale. Based on this concept, the proposed metric is defined as relative blur with respect to certain object scale using an absolute blur measure. The object scale is characterized by a granularity analysis of image content. And the absolute blur is built on an analysis of edge local gray level distribution. The performance of the metric is tested and compared with some outstanding existing metrics in this field on three widely used databases. The experiment results show that the proposed metric can predict the sharpness of images in varying databases with high accuracy and reliability.     

沙尘环境下视频图像增强方法的研究

针对沙尘暴环境下,因大气介质中悬浮的沙尘颗粒的散射作用导致的视频图像质量严重下降,清晰度与对比度降低,边缘模糊等问题,本文提出了一种空间域与变换域相结合的沙尘图像增强方法。先将降质图像转换到模糊域进行全局沙尘图像的PAL模糊增强;然后在空间域利用带限局部直方图自适应均衡算法对局部分量进行增强处理,最后利用POSHE算法对局部细节再次进行增强处理。实验结果表明,所提方法能够有效的提高沙尘图像的对比度,突出图像细节与边缘信息,增强了图像的整体视觉效果。     

Edge-based motion compensated classified DCT with quadtree for image sequence coding

The motion compensated discrete cosine transform coding (MCDCT) is an efficient image sequence coding technique. In order to further reduce the bit-rate for the quantizied DCT coefficients and keep the visual quality, we propose an adaptive edge-based quadtree motion compensated discrete cosine transform coding (EQDCT). In our proposed algorithm, the overhead moving information is encoded by a quadtree structure and the nonedge blocks will be encoded at lower bit-rate but the edge blocks will be encoded at higher bit-rate. The edge blocks will be further classified into four different classes according to the orientations and locations of the edges. Each class of edge blocks selects the different set of the DCT coefficients to be encoded. By this method, we can just preserve and encode a few DCT coefficients, but still maintain the visual quality of the images. In the proposed EQDCT image sequence coding scheme, the average bit-rate of each frame is reduced to 0.072 bit/pixel and the average PSNR value is 32.11 dB.     

基于SUSAN算子的空域自适应错误隐藏算法

为了消除空域错误隐藏后出现的模糊和块效应,提出了基于SUSAN检测算子的空域自适应错误隐藏算法。算法首先利用SUSAN算子检测丢失宏块周围的边缘点,然后根据边缘点确定丢失宏块中边缘,最后根据边缘情况自适应地选择插值方法恢复丢失宏块。实验表明,丢失宏块边缘的检测受噪声影响很小,对孤立丢失块有较好的恢复效果且计算量较小。与传统空域错误隐藏算法相比,本文算法的峰值信噪比PSNR能提高大约1～3dB。     

A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)

This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of just noticeable blur into a probability summation model. Unlike existing objective no-reference image sharpness/blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual-based sharpness metric. These results show that the proposed sharpness metric correlates well with the perceived sharpness being able to predict with high accuracy the relative amount of blurriness in images with different content.      

基于四元数奇异值分解的视频质量评价方法

准确的客观视频质量评价方法对于视频应用发展是至关重要的.近年来,图像质量评价方法已经比较成熟,而视频质量评价方法与图像质量评价方法在性能上的差距仍然较大.本文提出一种基于四元数奇异值分解的客观视频质量评价方法,该方法将像素的亮度、色度、边缘能量和残差能量作为四元数的四个部分,并用熵作为视觉感兴趣系数对块加权.在视频质量...     

Classified vector quantisation with variable block-size DCT models

The paper describes the classified vector quantisation (CVQ) of an image, based on quadtrees and a classification technique in the discrete cosine transform (DCT) domain. In this scheme, a quadtree is used to segment low-detail regions into variable sized blocks and high-detail regions into uniform 4×4 blocks of various edge and mixed classes. High-detail blocks are classified by an edge-oriented classifier which employs a pattern-matching technique with edge models defined in the normalised DCT domain. The proposed classifier is simple to implement, and efficiently classifies edges to good visual accuracy. The low-detail regions are encoded at very low bit rates with little perceptual degradation, while the encoding of the high-detail regions is performed to achieve a good perceptual quality in the decoded image. Decoded images of high visual quality are obtained for encoding rates between 0.3 and 0.7 bpp     

The accuracy of PSNR in predicting video quality for different video scenes and frame rates

Peak Signal-to-Noise Ratio (PSNR) is widely used as a video quality metric or performance indicator. Some studies have indicated that it correlates poorly with subjective quality, whilst others have used it on the basis that it provides a good correlation with subjective data. Existing literature seems to provide conflicting evidence of the accuracy of PSNR as a video quality metric. Based on experimental results, we explain a scenario where PSNR provides a reliable indication of the variation of subjective video quality and scenarios where PSNR is not a reliable video quality metric. We show that PSNR follows a monotonic relationship with subjective quality in the case of full frame rate encoding when the video content and codec are fixed. We provide evidence that PSNR becomes an unreliable and inaccurate quality metric when several videos with different content are jointly assessed. Furthermore, PSNR is inaccurate in measuring video quality of a video content encoded at different frame rates because it is not capable of assessing the perceptual trade-off between the spatial and temporal qualities. Finally, where PSNR is not a reliable video quality metric across different video contents and frame rates, we show that a perceptual video model recently approved by the International Telecommunication Union (ITU) provides quality predictions highly correlating with subjective scores even if different video scenes coded at different frame rates are considered in the test set.     

Shape-VQ-based lossless hybrid ADPCM/DCT coder

The discrete cosine transform (DCT) has been shown as an optimum encoder for sharp edges in an image (Andrew and Ogunbona, 1997). A conventional lossless coder employing differential pulse code modulation (DPCM) suffers from significant deficiencies in regions of discontinuity, because the simple model cannot capture the edge information. This problem can be partially solved by partitioning the image into blocks that are supposedly statistically stationary. A hybrid lossless adaptive DPCM (ADPCM)/DCT coder is presented, in which the edge blocks are encoded with DCT, and ADPCM is used for the non-edge blocks. The proposed scheme divides each input image into small blocks and classifies them, using shape vector quantisation (VQ), as either edge or smooth. The edge blocks are further vector quantised, and the side information of the coefficient matrix is saved through the shape-VQ index. Evaluation of the compression performance of the proposed method reveals its superiority over other lossless coders     

基于FPGA的红外图像细节增强算法设计与实现

双边滤波算法是一种有效的红外图像细节增强算法,具有保边去噪的效果。但由于算法运算量大,在红外视频图像处理中较难实现。本文提出了一种双边滤波+平台直方图均衡的红外图像增强算法的FPGA实现方法,选用Xilinx Virtex-5系列芯片,采用流水线和并行处理技术,能够在40 ms内完成一帧640×480的14位图像的处理,有效提升红外图像的清晰度和对比度,并满足视频图像处理算法的实时性要求。