Cross residual transform for lossless intra-coding for HEVC

A new lossless intra-coding method based on a cross residual transform is applied to the next generation video coding standard HEVC (High Efficiency Video Coding). HEVC includes a multi-directional spatial prediction method to reduce spatial redundancy by using neighboring pixels as a prediction for the pixels in a block of data to be encoded. In the new lossless intra-coding method, the spatial prediction is performed as pixelwise DPCM but is implemented as block-based manner by using cross residual transform on the HEVC standard. The experimental results show that the new lossless intra-coding method reduces the bit rate of approximately 8.43% in comparison with the lossless-intra coding method in the HEVC standard and the proposed method results in slightly better compression ratio than the JPEG200 lossless coding.     

Fast motion and disparity estimation for HEVC based 3D video coding

The emerging international standard for high efficiency video coding (HEVC) based 3D video coding (3D-HEVC) is an extension of HEVC. In the test model of 3D-HEVC, variable size motion estimation (ME) and disparity estimation (DE) are both employed to select the best coding mode for each treeblock in the encoding process. This technique achieves the highest possible coding efficiency, but it brings extremely high computational complexity which limits 3D-HEVC from practical applications. In this paper, a fast ME/DE algorithm based on inter-view and spatial correlations is proposed to reduce 3D-HEVC computational complexity. Since the multi-view videos represent the same scene with similar characteristic, there is a high correlation among the coding information from inter-view prediction. Besides, the homogeneous regions in texture video have a strong spatial correlation, and thus spatially neighboring treeblocks have similar coding information. Therefore, we can determine ME search range and skip some specific ME and DE rarely used in the previously coded view frames and spatially neighboring coding unit. Experimental results demonstrate that the proposed algorithm can significantly reduce computational complexity of 3D-HEVC encoding while maintaining almost the same rate-distortion performance.     

Fast algorithm based on the sole- and multi-depth texture measurements for HEVC intra coding

In High Efficiency Video Coding (HEVC), intra coding plays an important role, but also involves huge computational complexity due to a flexible coding unit (CU) structure and a large number of prediction modes. This paper presents a fast algorithm based on the sole- and multi-depth texture measurements to reduce the complexity from CU size and prediction mode decisions. For the CU size decision, evaluation results in the CU coding with one and multiple depths are utilized to classify CUs into heterogeneous, homogeneous, depth-prominent and other ones. Fast CU size decisions are made for different kinds of CUs. For the prediction mode decision, the tendencies for different CU sizes are detected based on multiple depths. The number of searching modes is decreased adaptively for the CU size with fewer tendencies. Experimental results show the proposed algorithm by off-line training reduces 53.32% computational complexity, with 1.47% bit-rate increasing.     

Fast CU size and prediction mode decision method for HEVC encoder based on spatial features

In the high-efficiency video coding (HEVC) standard, intra prediction has higher computational complexity compared with H.264/AVC (advanced video coding) because of increasing the number of intra prediction modes and also higher number of coding unit (CU) sizes. The HEVC encoder evaluates 35 prediction modes on five possible prediction unit (PU) sizes to find the one with the minimum rate–distortion cost. Although this approach improves coding efficiency, it is very time-consuming. In this paper, we propose a fast intra prediction method to reduce the complexity of I-frame coding. The proposed method consists of three stages which is based on smoothness spatial feature. In the first stage, a measure is introduced to estimate CU smoothness by using sum of absolute differences (SAD) among CU pixels in four directions. By considering that a smooth region can be predicted with larger CUs, when the measured smoothness parameter is lower than a predefined threshold, only the prediction modes in the current CU are evaluated. In the second stage, the number of intra prediction modes is reduced based on the calculated SADs in the previous stage. In the last stage, if the first three candidate modes resulted from rough mode decision stage in the previous PU and the current PU are similar, then the best mode prediction of the previous PU is selected as the best candidate mode. Experimental results indicate that the proposed method can reduce the coding time on average to 43 % and maintain coding video quality, whereas bitrate increases negligibly (0.5 %).     

基于ROA-MSP的激光图像边缘检测方法

传统的激光图像边缘检测方法受到图像分辨率低、边缘模糊等因素的影响,因此,设计ROA-MSP的激光图像边缘检测方法.首先分析了激光图像成像原理,分析了其在成像过程中的影响因素,然后对激光图像的滤波处理,在此基础上对图像形态学处理,对信号进行变换,并建立调节函数,对图像增强处理,最后采用ROA-MSP算法,制定检测流程,以...     

基于Canny边缘检测和外观特征的微血管瘤检测方法

糖尿病性视网膜病变进行早期筛查可以减少疾病的发展并且阻止随后的视力损害。微血管瘤是糖尿病性视网膜病变的早期临床症状,可以通过微血管瘤检测对糖尿病性视网膜病变进行早期筛查。针对眼底图像中视网膜血管、视盘、渗出物以及微血管瘤之间的相互关系,在红色通道和绿色通道加权图上定位出视盘,在绿色通道上采用基于简单统计的自适应双阈值Canny算子进行边缘检测,并进行封闭区域的填充。设定阈值消除大面积对象并移除视网膜血管、视盘和渗出物得到微血管瘤的候选区域,最后根据形状特征和颜色特征从候选区域中得到真正的视网膜微血管瘤。实验结果表明,该算法能够有效提取视网膜眼底图像中的微血管瘤,敏感性和阳性预测值分别达到92%和86%,优于现有一些典型的微血管瘤检测方法,能够精确地检测出微血管瘤,可用在糖尿病性视网膜病变早期筛查中。     

Image coding based on adaptive set partitioning

An image coding scheme using zero blocks with novel structure is proposed, where the context of child nodes is considered in the set partitioning to meet the characteristics of wavelet coefficients.     

一种快速HEVC帧内预测算法

为了提高HEVC帧内预测的编码速度,提出了一种快速帧内预测算法.该算法充分利用视频序列的时间相关性,利用相邻帧CU的大小相关性在CU(Code Unit)层提前判定CU的最佳编码尺寸以及利用相邻帧在PU层减少预测方向数目来加速帧内预测的过程.实验结果表明:提出的算法与HEVC已有快速算法相比,峰值信噪比(PSNR)平均下降0.06 dB,输出码率平均减少0.09%,而帧内预测时间平均减少了约49.81%.     

An enhanced entropy coding scheme for HEVC

This paper presents a modification to Context-based Adaptive Binary Arithmetic Coding (CABAC) in High Efficiency Video Coding (HEVC), which includes an improved context modeling for transform coefficient levels and a binary arithmetic coding (BAC) engine with low memory requirement. In the improved context modeling for transform coefficient levels, the context model index for significance map is dependent on the number of the significant neighbors covered by a local template and its position within transform block (TB). To limit the total number of context models for significance map, TBs are split into different regions based on the coefficient position. The same region in different TBs shares the same context model set. For the first and second bins of the truncated unary scheme of absolute level minus one, their context model indices depend on the neighbors covered by a local template of the current transform coefficient level. Specifically, the context model index for the first bin is determined by the number of neighbors covered by the local template with absolute magnitude equal to 1 and larger than 1; for the second bin, its context model index is determined by the number of neighbors covered by the local template with absolute magnitude larger than 1 and larger than 2. Moreover, TB is also split into different regions to incorporate the coefficient position in the context modeling of the first bin in luma component. In the BAC engine with low memory requirement, the probability is estimated based on a multi-parameter probability update mechanism, in which the probability is updated with two different adaption speeds and use the average as the estimated probability for the next symbol. Moreover, a multiplication with low bit capacities is used in the coding interval subdivision to substitute the large look-up table to reduce its memory consumption. According to the experiments conducted on HM14.0 under HEVC main profile, the improved context modeling for transform coefficient levels achieves 0.8%, 0.6% and 0.4% bitrate reduction on average for all intra (AI), random access (RA) and low delay (LD) configurations, respectively; the BAC engine with low memory requirement achieves 0.7%, 0.6% and 0.5% bitrate reduction on average for AI, RA and LD configurations, respectively; the overall bitrate reduction achieved by the proposed two techniques is 1.4%, 1.1% and 0.9% on average for AI, RA and LD configurations, respectively.     

基于Roberts边缘检测的数字媒体激光重现方法

为提高三维激光重现技术在特定光照条件下角分辨率的精度,研究基于Roberts边缘检测的数字媒体激光重现方法。搭建三维信息主动获取结构,分析编码光条纹偏移的关系,并根据条纹编码设计二进制码字的排列分布,完成结构光的编码和解码;提取边缘特征,引入Roberts边缘检测算子,检测对角方向上两个相邻像素差,分析尺度空间中检测点的分布,采用检测器提高稳定性,生成特征点描述子,完成数字媒体三维激光重现。实验结果显示,设计的技术具有更高的角分辨率精度,在92%以上,平均成像误差为1.24像素,成像时间为3 s。     

Fast inter-prediction mode decision algorithm for HEVC

The high efficiency video coding (HEVC) is superior to the previous video coding standards in compression performance, while the computational complexity is introduced simultaneously. The complexity increases mainly due to the novel flexible partitioning scheme that allows the inter-prediction mode partition split via exhaustive rate-distortion optimization (RDO). In this paper, a fast inter-prediction mode decision algorithm is proposed, which contains adaptive threshold determination based on quantization parameter and fast inter-prediction mode partition decision. The proposed algorithm utilizes the edge information of the partition to simplify the RDO and then accelerates coding time of inter-prediction mode decision structure for the original HEVC encoder. The experimental results show that the proposed algorithm can achieve 39.5 % coding time reduction with just 1.97 % bitrate increase on average under random access condition and 35.2 % coding time reduction with just 1.89 % bitrate increase on average under low-delay B condition, compared to the original HEVC encoder.     

Flow-based relaxation method for edge detection

The authors propose a flow-based edge relaxation algorithm which can eliminate the unrealisable edge patterns that may arise in the non-directional edge relaxation process. The experimental results showed that the detected edges are more reliable than those found by the non-directional edge relaxation algorithm     

Efficient matching pursuit image coding based on block partitioning

A novel matching pursuit (MP) image coding method is presented. A block partitioning scheme is employed to effectively exploit the spatial characteristics of MP atoms. Experimental results show that the proposed method significantly outperforms the existing MP coding methods and thus bridges the gap in coding efficiency between MPand wavelet-based approaches.     

HEVC帧内CU划分快速算法研究

为了降低帧内编码单元(CU)四叉树划分的计算复杂度,本文提出了一种基于多重纹理的帧内CU划分快速算法.通过提取CU的内容复杂度、方向复杂度以及子CU的内容复杂度差异和方向复杂度差异作为纹理特征,设计了基于双SVM的CU划分决策模型,实现了对复杂纹理CU的提前划分和简单纹理CU的提前终止划分.实验结果表明,该快速算法与x265-1.7相比,码率增加0.47％,亮度峰值信噪比降低0.012 dB,编码时间减少36.87％,有效提高了帧内预测效率.     

双尺度灰色关联度的医学图像边缘检测方法

边缘检测是医学图像处理的关键技术之一,目的是在有噪声背景的图像中确定出目标物边界的位置.鉴于传统的边缘检测算子在医学图像中不能达到很好的效果,本文研究了灰色理论中灰色关联度在医学图像中的应用,采用了一种基于灰色关联度的图像增强以及图像处理区域选取的双尺度方法用于医学图像的边缘检测中.实验结果表明这种方法能够达到既增强了...     

Rate model considering inter-symbol dependency for HEVC inter-frame coding

In this paper, an accurate rate model is proposed for inter-frame coding of high-efficiency video coding, which is useful for rate control. The proposed model considers the effect of entropy coding where the inter-symbol dependency is exploited in context- adaptive binary arithmetic coding (CABAC) for saving coded bits. The mutual information is first predicted to measure the reduction of uncertain information in CABAC, and then the conditional entropy is calculated to estimate the output bit-rate of inter-frame residues. Since the source characteristic also significantly impacts on the building of rate model, a joint Laplacian distribution source at the transform unit levels is employed in the proposed rate model. The experimental results show that the proposed model achieves a better rate-distortion performance in rate control. The proposed approach can be also extended to other video codecs using CABAC for the design of rate models.     

基于边缘保持滤波的Canny彩色图像边缘检测方法

提出了基于边缘保持滤波的Canny彩色图像边缘检测方法。该方法利用了HSV颜色空间信道相关性低的优点,结合Canny算子定位准确的优点和边缘保持滤波理论,用边缘保持滤波取代传统的高斯滤波,用梯度矢量计算法替代传统的梯度标量计算法,从而增强了在平滑过程中对图像边缘的保持,最大程度保留了色彩的差异信息,实现了彩色图像边缘的自适应提取。实验结果证明,该方法将灰度空间的Canny算法推广到彩色矢量空间,充分利用了彩色信息,对彩色图像边缘提取具有较好的检测精度和准确度。     

A robust method based on ICA and mixture sparsity for edge detection in medical images

In this paper, a robust edge detection method based on independent component analysis (ICA) was proposed. It is known that most of the ICA basis functions extracted from images are sparse and similar to localized and oriented receptive fields. In this paper, the L ^p norm is used to estimate sparseness of the ICA basis functions, and then, the sparser basis functions were selected for representing the edge information of an image. In the proposed method, a test image is first transformed by ICA basis functions, and then, the high-frequency information can be extracted with the components of the selected sparse basis functions. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in the ICA domain, we can readily obtain the sparse components of the noise-free image, resulting in a kind of robust edge detection even for a noisy image with a very low SN ratio. The efficiency of the proposed method for edge detection is demonstrated by experiments with some medical images.