低码率视频的小波域软阈值去方块滤波算法的研究

本文针对低码率条件下基于子块DCT的视频压缩产生的方块效应,提出了一种小波域上进行软阈值滤波的改进算法。新算法根据图像的平坦区和单调变化区等非纹理区易于通过平滑滤波去噪的性质,以非纹理区作为参考对象计算阈值,并且采用虚拟滤波的思想简化了阈值的计算,同时根据低码率压缩视频的特点修改了在低层子带的软阈值处理过程。对测试序列Susan的实验结果表明,新的算法不仅获得很好的主观去方块效果,而且可以对帧内和帧间编码模式平均分别获得约1.0dB和0.28dB的客观质量(PSNR)的改善,因此新的算法适用于可视电话等低码率视频的应用。     

一种有效的空域去块效应算法

块效应在低比特率图像中极为明显,提出一种基于方块方向活动性的自适应空域去块效应算法。首先,通过对偏移的块进行块分析来对每一个块进行模式选择,接着通过1-D自适应平滑滤波器对块效应进行滤波。实验结果表明,与之前的算法相比,该算法有效的去除了块效应,信噪比有较大的提高,且算法同时处理了一个块中的竖直和水平边界,再跳入下一个块,大大减小了硬件实现时的内存空间。     

针对MPEG的去块效应技术的研究及其VLSI实现

现今随着数据压缩的广泛应用,以及MPEG视频编解码的普遍使用,块效应噪声也随之而来。为了消除块效应、提高视频质量,许多去块效应的算法[2-12]被人们所提出,然而大部分算法的运算量都很大,不适合应用在视频处理芯片上。针对这个问题,H.-S. Kong等人[13]提出了一种新的自适应后处理算法,该算法既能有效消除块效应又大幅度地降低了运算量,然而对图像边缘信息保护不够。本文在H.-S. Kong等人[13]的算法的基础上,提出了一种针对MPEG视频流的去方块后处理方法。该方法以方块边缘（水平或垂直）附近各两个4×4区域作为滤波处理的分类依据,通过对该两个区域的像素点的值进行计算,将块边缘分为平滑渐变区域、亮度阶跃区域以及复杂纹理区域,并根据这三种区域对图像边缘信息影响程度的大小,而决定采取强滤波、弱滤波或者不滤波等处理方式。这种方法一方面较好地提取了块效应噪声,一方面又保护了图像的边缘信号,而且计算量较小,满足视频处理芯片低复杂度的去噪要求,通过VLSI实现后有着很好的应用前景。      

一种针对wedgelet重建图像的空域自适应去块效应算法

提出一种针对wedgelet重建图像的空域自适应去块效应算法.算法针对不同图像块之间块效应的强弱程度,合理调整块边缘滤波像素的个数,从而实现自适应地消除块效应.仿真结果表明,该算法在较好地保留了图像的纹理和边缘的同时能有效地去除图像的块效应.     

基于块编码视频的无参考质量评估

该文提出了一种适用于基于块编码视频的无参考质量评估方法。首先结合人类视觉的亮度掩盖和对比度掩盖特性提出了一个符合主观视觉感知的方块效应测度,然后根据滤波对方块效应的影响,给出了一种适合于使用不同压缩和处理算法的基于块编码重构视频的质量评估方法。实验表明该质量评估测度与主观质量评估有较好的一致性。     

一种基于块局部最优维纳滤波的图像重构算法

基于块的图像处理方法将欧氏距离作为相似块的评判标准,无法很好地反映块之间的结构性,导致重构图像存在不同程度的块效应。该文根据基于块的维纳滤波器能够充分利用图像块冗余度的特点,结合块效应的空间分布特征,提出一种改进的基于块局部最优维纳滤波的图像重构算法。首先,该算法通过对图像高频部分稀疏采样,将块效应限定在块与块的交界处,然后将图像分成块边界区域和块中心区域,利用几何结构相似和亮度相似的图像块确定滤波参数,进而平滑块效应。实验结果表明,该算法可以有效抑制重构过程中的块效应,且对于细节丰富的图像,重构效果的提升更加明显。     

一种灵活的去块滤波算法

利用对图像的实际边界进行有效的辨别,在原来H.264去块环路滤波的基础上改进算法,可以较好地滤除块效应,使得去块滤波算法可以更加灵活地应用.通过用多种码流测试仿真得出的数据显示,相对于原来的滤波算法峰值信噪比(PSNR)改变较小,主观上图像的视觉效果有较好的改善.     

一种基于无参考的块效应评价方法

针对基于离散余弦变换(DCT)变换的图像压缩后失真图像易产生块效应,提出了一种有效的块效应评价方法.首先依据JPEG图像每8×8图像块的灰度值特性把其失真程度分为严重、较严重、有轻微方块化效应和无方块化4类,统计每类图像块占整幅图像的比例,然后确立每类图像块与图像质量平均主观得分(MOS)之间的关系,利用统计的Regr...     

基于DCT域的块效应消除算法

基于块的DCT图像压缩方法通常会因为系数的粗糙量化导致块与块之间出现称之为块效应的不连续现象。本文提出了一种新的基于DCT域的块效应消除算法，目的是实现在尽量消除块效应的同时充分地人口出生率图像的边缘信息。仿真结果表明，本文提出的块效应消除算法比原来的算法在不同压缩比特率下，对不同的图像，都能得到更好的效果。     

H．264中图像去方块的编码算法研究

为了解决视频编解码后图像出现方块的效应，文章通过研究与对比现有的去块算法，提出适于H．264标准的改进图像去方决算法。详细阐述算法的改进思想和实现过程，给出了改进后新的处理顺序，对边界去块强度计算公式进行推导。最后对改进算法进行性能测试，结果表明算法运行时间减法短，存储空间减少，性能大大提高。     

一种自适应消除块效应的新算法

基于块DCT图像压缩方法通常会因比特率低而导致块效应.本文提出一种新的块效应消除算法,目的是既能消除块效应又能充分保护图像的边缘和纹理.算法首先引入均匀块的定义,并给出一种阈值可变的判断转换块是否均匀块的标准.对均匀块的每一行(或列)分别使用自适应的Sigmoid函数代替阶梯函数,以消除块效应;而对非均匀块使用Sigma滤波器进行平滑.仿真结果表明,本文的算法相比Liu(2002)和Luo(2003)提出的相应算法,有更良好的性能.     

一种基于HVS和熵分析的盲测量去块效应算法

针对离散余弦变换基于块(DCT)图像压缩的方法通常因比特率低而导致块效应,结合人类视觉系统(HVS)和局部熵分析,提出一种新的去块效应算法。算法首先引入基于HVS的块效应可见度函数,其次通过设定阈值对需要处理的块进行熵的计算从而进行纹理块和平滑块分类,最后用自适应滤波器对不同类型块进行滤波。仿真结果表明,本文算法既能消除块效应,又能充分保护图像的纹理。     

基于加权引导滤波与时域高通滤波的非均匀性校正算法

为解决基于传统时域高通滤波红外图像非均匀性校正算法存在鬼影现象以及固定图案噪声去除不彻底的问题,提出了一种加权引导滤波和改进时域高通滤波相结合的非均匀性校正算法。首先,利用加权引导滤波准确分离红外图像中的空域高频成分;然后,计算红外图像中像素点灰度值变化幅度;最后,在进行时域高通滤波时对红外图像中的运动区域和静止区域使用不同时间常数进行校正。采用两组真实红外图像序列进行实验,并与经典的双边滤波时域高通,均值滤波时域高通非均匀性校正算法进行比较。实验结果表明:文中所提算法在主观视觉和客观评价指标方面优于其他两种算法,有效降低了红外图像的非均匀性,不会产生鬼影,取得了较好的非均匀性校正效果。     

Removing the blocking artifacts of block-based DCT compressed images

One of the major drawbacks of the block-based DCT compression methods is that they may result in visible artifacts at block boundaries due to coarse quantization of the coefficients. We propose an adaptive approach which performs blockiness reduction in both the DCT and spatial domains to reduce the block-to-block discontinuities. For smooth regions, our method takes advantage of the fact that the original pixel levels in the same block provide continuity and we use this property and the correlation between the neighboring blocks to reduce the discontinuity of the pixels across the boundaries. For texture and edge regions, we apply an edge-preserving smoothing filter. Simulation results show that the proposed algorithm significantly reduces the blocking artifacts of still and video images as judged by both objective and subjective measures.     

New Blocking Artifacts Reduction Method Based on Wavelet Transform

It is well known that a block discrete cosine transform compressed image exhibits visually annoying blocking artifacts at low-bit-rate. A new post-processing deblocking algorithm in wavelet domain is proposed. The algorithm exploits blocking-artifact features shown in wavelet domain. The energy of blocking artifacts is concentrated into some lines to form annoying visual effects after wavelet transform. The aim of reducing blocking artifacts is to capture excessive energy on the block boundary effectively and reduce it below the visual scope. Adaptive operators for different subbands are computed based on the wavelet coefficients. The operators are made adaptive to different images and characteristics of blocking artifacts. Experimental results show that the proposed method can significantly improve the visual quality and also increase the peak signal-noise-ratio（PSNR） in the output image.     

An Objective Video Quality Metric for Compressed Stereoscopic Video

Stereoscopic video create the impression of depth by using two slightly different viewpoints. Conventional video quality assessment methods for 2D video are not suitable for stereoscopic video, so a new video quality assessment model for stereoscopic video is needed. In this paper, we propose a new objective video quality metric for compressed stereoscopic video. The proposed algorithm uses blocking artifacts, blurring in edge regions and video quality difference between two views. Blocking artifacts and blurring in edge regions are distortion appeared in the compressed video, and they are widely used in conventional video quality model. Difference in video quality between two views considers 3D effect of stereoscopic video. To verify the performance of the proposed algorithm, we performed subjective evaluation of stereoscopic video, and we confirmed that the proposed algorithm is superior to the conventional algorithms in respect to correlation with the subjective evaluation.     

Self-learning-based post-processing for image/video deblocking via sparse representation

Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a common problem in block-based image/video compression, especially at low bitrate coding. Various post-processing techniques have been proposed to reduce blocking artifacts, but they usually introduce excessive blurring or ringing effects. This paper proposes a self-learning-based post-processing framework for image/video deblocking by properly formulating deblocking as an MCA (morphological component analysis)-based image decomposition problem via sparse representation. Without the need of any prior knowledge (e.g., the positions where blocking artifacts occur, the algorithm used for compression, or the characteristics of image to be processed) about the blocking artifacts to be removed, the proposed framework can automatically learn two dictionaries for decomposing an input decoded image into its “blocking component” and “non-blocking component.” More specifically, the proposed method first decomposes a frame into the low-frequency and high-frequency parts by applying BM3D (block-matching and 3D filtering) algorithm. The high-frequency part is then decomposed into a blocking component and a non-blocking component by performing dictionary learning and sparse coding based on MCA. As a result, the blocking component can be removed from the image/video frame successfully while preserving most original visual details. Experimental results demonstrate the efficacy of the proposed algorithm.     

Adaptive deblocking method for low bitrate coded HEVC video

HEVC in-loop deblocking filter significantly improves the subjective quality of coded video by removing blocking artifact. However, there are still visible blocking artifacts in the complex videos with fast and chaotic motions coded at a low bitrate. In this paper, we propose a three-step deblocking filter scheme, which pre-processes video to remove undesired noise, next removes the corner outliers, and then suppresses the normal blocking artifacts with adaptive deblocking filters. The whole deblocking filtering process is applied on both luma and chroma components. Experimental results show that the proposed method could effectively improve the subjective quality for various videos, and outperform other typical post-processing deblocking methods.     

Vision-model-based impairment metric to evaluate blocking artifactsin digital video

In this paper investigations are conducted to simplify and refine a vision-model-based video quality metric without compromising its prediction accuracy. Unlike other vision-model-based quality metrics, the proposed metric is parameterized using subjective quality assessment data recently provided by the Video Quality Experts Group. The quality metric is able to generate a perceptual distortion map for each and every video frame. A perceptual blocking distortion metric (PBDM) is introduced which utilizes this simplified quality metric. The PBDM is formulated based on the observation that blocking artifacts are noticeable only in certain regions of a picture. A method to segment blocking dominant regions is devised, and perceptual distortions in these regions are summed up to form an objective measure of blocking artifacts. Subjective and objective tests are conducted and the performance of the PBDM is assessed by a number of measures such as the Spearman rank-order correlation, the Pearson correlation, and the average absolute error The results show a strong correlation between the objective blocking ratings and the mean opinion scores on blocking artifacts     

Low complexity deblocking method for DCT coded video signals

A new method to remove blocking artefacts in low bit-rate block-based video coding, such as MPEG-4 and H.264, is presented. A low computational deblocking filter with five modes is proposed, including three frequency-related modes (smooth mode, intermediate mode, and complex mode for low-frequency, mid-frequency, and high-frequency regions, respectively), one special mode (steep mode for a large offset between two blocks) and a refined mode (corner mode for the corner of four blocks). A mode decision procedure is also needed to decide which mode is given by observing pixel behaviour around the block boundary. To take the masking effect of the human visual system (HVS) into consideration, the filter for smooth mode is designed to be much stronger than that for complex mode, because human eyes are more sensitive to smooth regions. Experimental results show that, in most cases, the proposed algorithm removes more blocking artefacts than MPEG-4 deblocking filters do, and improves both subjective and objective image quality. The proposed algorithm keeps the computation lower than MPEG-4 and is suitable for most block-based image and video coding systems.