Using edge direction information for measuring blocking artifacts of images

Block-based transform coding is the most popular approach for image and video compression. The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video coding systems. This paper presents a new algorithm for measuring blocking artifacts in images and videos. Instead of using the traditional pixel discontinuity along the block boundary, we use the edge directional information of the images. The new algorithm does not need the exact location of the block boundary thus is invariant to the displacement, rotation and scaling of the images. Experiments on various still images and videos show that the new blockiness measure is very efficient in terms of computational complexity and memory usage, and can produce blocking artifacts measurement consistent with subjective rating.     

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.     

GridSAR: Grid strength and regularity for robust evaluation of blocking artifacts in JPEG images

Images are subject to blocking artifacts when they are compressed using the JPEG standard. Knowing the extent of blocking artifacts is thus necessary for such applications as automatic quality monitoring and restoration. The current blocking artifacts measures are based on a strong prior that the block boundaries are known in advance, which often does not hold in real-world applications. Therefore, their performances degrade significantly when block boundaries are misaligned. To address the problem, this paper presents a robust no-reference blocking artifacts evaluation metric for JPEG images based on grid strength and regularity (GridSAR). The underlying idea is to extract block grids from a JPEG image and quantify the grid image to evaluate the strength of blocking artifacts. To this end, a grid map of blocking artifacts is first extracted from the image in the spatial domain. Then the blocking artifacts of the image are evaluated by quantifying the strength and regularity of the grid image. Furthermore, in order to account for the varying sensitivities of human eyes to the blocking artifacts in smooth and textured areas, a masking function is also proposed. Experimental results on seven popular image quality databases demonstrate that GridSAR achieves the state-of-the-art performance, and is robust to block misalignments.     

A locally adaptive algorithm for measuring blocking artifacts in images and videos

Block transform coding is the most popular approach for image and video compression. The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video coding systems. This paper presents a new algorithm for measuring image quality of a BDCT coded images or videos. It exhibits unique and useful features: (1) it examines the blocks individually so that it can measure the severity of blocking artifacts locally; (2) it is a one-pass algorithm in the sense that the image needs to be accessed only once; (3) it takes into account the blocking artifacts for high bit rate images and the flatness for the very low bit rate images; (4) the quality measure is well defined in the range of 0–10. Experiments on various still images and videos show that the new quality measure is very efficient in terms of computational complexity and memory usage, and can produce consistent blocking artifacts measurement.     

Tetrolet变换方块效应改善算法

针对Tetrolet 变换算法对图像去噪后存在方块效应的缺陷,文中对Tetrolet 变换算法进行了扩展和改进,并引入移位（Cycle Spinning）来有效消除Tetrolet 变换算法中的方块效应。仿真结果表明,所提出的算法不仅能有效去除噪声,而且可得到更高的峰值信噪比,提高了图像的主客观质量。去噪后图像保留了原始图像的边缘和细节等局部特征,较为平滑,且方块效应得到了一定的改善和抑制,因而该算法是有效可行的。     

Restoration of differential images for enhancement of compressed video

A modified regularization algorithm is proposed to enhance compressed video by restoring predictive-coded pictures. Since most video coding standards adopt a hybrid structure of macroblock-based motion compensation and block discrete cosine transform, the blocking artifacts occurs at both the block boundary and block interior, and the degradation process due to quantization is generated on differential images. Based on this observation, a new degradation model of differential images is presented first. Then the corresponding restoration algorithm directly processes the differential images before reconstructing decoded images. Two constraints, such as directional continuities on the block boundary and on the block interior, have been used for defining convex sets for restoring differential images. The proposed differential domain restoration algorithm is compared with the corresponding reconstructed domain algorithm using the same degradation model and equivalent set of constraints. The proposed algorithm outperforms the reconstructed domain algorithm in both analytic and experimental senses.     

一种针对DCT域高压缩图像的去块效应算法

在基于块DCT编码的图像压缩技术中,高压缩图像的块边界会出现块效应,严重损害图像的主观质量.提出一种新的基于图像恢复的去块效应算法,该算法根据图像特征从已分类的图像库中进行匹配,进而对图像噪声进行估计,达到去块效应的效果.经过实验证明,该算法有效.     

Single image reflection removal based on structure-texture layering

In computer vision, the reflections impair the overall perception of images and introduce undesired interference to high-level image understanding. To date, reflection removal approaches have been developed with advantage of visual quality enhancement. However, the reflection removal for single image is still a challenging task with its performance to be further boosted. During this process, additional visual artifacts such as blocking may also be introduced that degrades subjective quality of images. In this paper, we make the first attempt to eliminate image reflections and artifacts simultaneously within a unified framework. A structure-texture strategy is utilized to decompose the image into structure and texture layers. The proposed model handles reflection removal in both layers and further suppress blocking artifacts in the texture layer. Finally, a fusion process is employed to obtain the result image. Experiments with objective and subjective evaluations validate the superiority of the proposed method, which outperforms several state-of-the-art algorithms.     

Encoding of images based on a lapped orthogonal transform

Traditional block transform image coding systems generate artifacts near block boundaries which degrade low bit rate coded images. To reduce these artifacts, a class of unitary transformations, called lapped orthogonal transforms (LOT), is investigated. The basis function on which the signal is projected are overlapped for adjacent blocks. An example of an LOT optimized in terms of energy compaction is numerically derived, using an augmented Lagrangian optimization algorithm. Using this LOT, intraframe coding experiments for 256×240 pixel images were performed at bit rates between 0.1 and 0.35 bits/pixel. The LOT improved the coded image subjective quality over other transforms. The LOT was also used in interframe full-motion video coding experiments for head and shoulder sequences at 28 and 56 kb/s. Significant improvement resulted at low data rates and if no motion compensation were used. However, the improvement was no longer significant at 56 kb/s with full motion compensation     

A Perceptual Approach to the Reduction of Blocking Effect in DCT-Coded Images

It is well known that blocking effects constitute one of the main drawbacks of discrete-cosine-transform-based lossy coding schemes. In this paper, we first propose a distortion measure for blocking artifacts in images. The visibility of blocking error is computed based on several characteristics of the observer's visual perception, and an image quality factor is derived based on the artifact visibilities. We then develop a novel approach for removing blocking effects in transform-coded images. The technique is based on a nonlinear, spatially variant adaptive smoothing operation which exploits the visibility measure of blocking artifacts present in the decoded image. It introduces no modification on actual image compression standards and allows one to reduce the blockiness without significantly blurring the image. Experimental results obtained at low bit rate show that both objective and subjective quality of the reconstructed images are greatly improved.     

Improvement of DCT-based compression algorithms using Poisson's equation.

We propose two new image compression-decompression methods that reproduce images with better visual fidelity, less blocking artifacts, and better PSNR, particularly in low bit rates, than those processed by the JPEG Baseline method at the same bit rates. The additional computational cost is small, i.e., linearly proportional to the number of pixels in an input image. The first method, the "full mode" polyharmonic local cosine transform (PHLCT), modifies the encoder and decoder parts of the JPEG Baseline method. The goal of the full mode PHLCT is to reduce the code size in the encoding part and reduce the blocking artifacts in the decoder part. The second one, the "partial mode" PHLCT (or PPHLCT for short), modifies only the decoder part, and consequently, accepts the JPEG files, yet decompresses them with higher quality with less blocking artifacts. The key idea behind these algorithms is a decomposition of each image block into a polyharmonic component and a residual. The polyharmonic component in this paper is an approximate solution to Poisson's equation with the Neumann boundary condition, which means that it is a smooth predictor of the original image block only using the image gradient information across the block boundary. Thus, the residual--obtained by removing the polyharmonic component from the original image block--has approximately zero gradient across the block boundary, which gives rise to the fast-decaying DCT coefficients, which, in turn, lead to more efficient compression-decompression algorithms for the same bit rates. We show that the polyharmonic component of each block can be estimated solely by the first column and row of the DCT coefficient matrix of that block and those of its adjacent blocks and can predict an original image data better than some of the other AC prediction methods previously proposed. Our numerical experiments objectively and subjectively demonstrate the superiority of PHLCT over the JPEG Baseline method and the improvement of the JPEG-compressed images when decompressed by PPHLCT.     

Adaptive depth truncation filter for MVC based compressed depth image

In multiview video plus depth (MVD) format, virtual views are generated from decoded texture videos with corresponding decoded depth images through depth image based rendering (DIBR). 3DV-ATM is a reference model for the H.264/AVC based multiview video coding (MVC) and aims at achieving high coding efficiency for 3D video in MVD format. Depth images are first downsampled then coded by 3DV-ATM. However, sharp object boundary characteristic of depth images does not well match with the transform coding based nature of H.264/AVC in 3DV-ATM. Depth boundaries are often blurred with ringing artifacts in the decoded depth images that result in noticeable artifacts in synthesized virtual views. This paper presents a low complexity adaptive depth truncation filter to recover the sharp object boundaries of the depth images using adaptive block repositioning and expansion for increasing the depth values refinement accuracy. This new approach is very efficient and can avoid false depth boundary refinement when block boundaries lie around the depth edge regions and ensure sufficient information within the processing block for depth layers classification. Experimental results demonstrate that the sharp depth edges can be recovered using the proposed filter and boundary artifacts in the synthesized views can be removed. The proposed method can provide improvement up to 3.25 dB in the depth map enhancement and bitrate reduction of 3.06% in the synthesized views.     

Blocking effect reduction of JPEG images by signal adaptivefiltering

A postprocessing algorithm is proposed to reduce the blocking artifacts of Joint Photographic Experts Group (JPEG) decompressed images. The reconstructed images from JPEG compression produce noticeable image degradation near the block boundaries, in particular for highly compressed images, because each block is transformed and quantized independently. The blocking effects are classified into three types of noises in this paper: grid noise, staircase noise, and corner outlier. The proposed postprocessing algorithm, which consists of three stages, reduces these blocking artifacts efficiently. A comparison study between the proposed algorithm and other postprocessing algorithms is made by computer simulation with several JPEG images.     

Image coding using lapped biorthogonal transform

The wireless sensor network utilizes image compression algorithms like JPEG, JPEG2000, and SPIHT for image transmission with high coding efficiency. During compression, discrete cosine transform (DCT)–based JPEG has blocking artifacts at low bit-rates. But this effect is reduced by discrete wavelet transform (DWT)–based JPEG2000 and SPIHT algorithm but it possess high computational complexity. This paper proposes an efficient lapped biorthogonal transform (LBT)–based low-complexity zerotree codec (LZC), an entropy coder for image coding algorithm to achieve high compression. The LBT-LZC algorithm yields high compression, better visual quality with low computational complexity. The performance of the proposed method is compared with other popular coding schemes based on LBT, DCT and wavelet transforms. The simulation results reveal that the proposed algorithm reduces the blocking artifacts and achieves high compression. Besides, it is analyzed for noise resilience.     

Image deblocking via sparse representation

Image compression based on block-based Discrete Cosine Transform (BDCT) inevitably produces annoying blocking artifacts because each block is transformed and quantized independently. This paper proposes a new deblocking method for BDCT compressed images based on sparse representation. To remove blocking artifacts, we obtain a general dictionary from a set of training images using the K-singular value decomposition (K-SVD) algorithm, which can effectively describe the content of an image. Then, an error threshold for orthogonal matching pursuit (OMP) is automatically estimated to use the dictionary for image deblocking by the compression factor of compressed image. Consequently, blocking artifacts are significantly reduced by the obtained dictionary and the estimated error threshold. Experimental results indicate that the proposed method is very effective in dealing with the image deblocking problem from compressed images.     

Perceivable artifacts in compressed video and their relation to video quality

Compressed video is degraded in quality due to the introduction of coding artifacts. A two-step subjective experiment was performed to evaluate the most visible artifacts and their relation to video quality for AVS and H.264 compressed video. In the first step, non-expert viewers were requested to score the image quality degradation as a function of compression ratio for various video sequences and to indicate which artifact was perceived during scoring. During the second step, eight trained viewers were asked to score the strength of three artifacts, i.e., blurring, blocking, and color distortion, which were reported as the most perceivable artifacts in the first step of the experiment. The quality performance between AVS and H.264 was also compared. The analysis of covariance indicated that the quality performance between AVS and H.264 was very close. A linear regression analysis showed that for the CIF videos 96% of the variance in quality degradation could be predicted by linearly combining the normalized strengths of the three most visible artifacts.     

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.     

针对一类JPEG图像伪造的被动盲取证

图像合成是一种最常见的图像伪造手段。该文针对一类JPEG图像合成伪造,根据篡改区域与非篡改区域块效应的不一致性,提出了一种简单有效的检测算法。首先利用估计的一次压缩质量因子对待检测图像进行裁剪再压缩,然后通过计算压缩前后的失真提取图像的块效应指数映射图,最后采用图像分割的方法实现篡改区域的自动检测与定位。实验结果表明,算法对于各种质量的JPEG图像和较小的篡改区域均能有效检测,当二次压缩与一次压缩的质量因子之差在15以上,虚警率控制在5%以内时,检测率可达93%以上。     

基于稠密矢量场及自适应补偿的帧率上变换

为了有效解决视频帧率上变换中物体边缘出现的模糊和块效应等问题,结合运动估计,优化了矢量化后处理和运动补偿算法.在基于块的运动估计中,结合运动的时空相关性优化块匹配算法,提高物体边缘运动矢量的准确性;通过基于像素块相关性的插值算法,获取像素矢量场,并保持物体结构完整性;在运动补偿时,结合矢量后处理的分类信息,自适应地进行加权插值计算,提高内插帧的质量.实验结果表明,与传统方法相比,该方法在主观和客观质量上都有较大的提升.     

View-spatial–temporal post-refinement for view synthesis in 3D video systems

Depth image based rendering is one of key techniques to realize view synthesis for three-dimensional television and free-viewpoint television, which provide high quality and immersive experiences to end viewers. However, artifacts of rendered images, including holes caused by occlusion/disclosure and boundary artifacts, may degrade the subjective and objective image quality. To handle these problems and improve the quality of rendered images, we present a novel view-spatial–temporal post-refinement method for view synthesis, in which new hole-filling and boundary artifact removal techniques are proposed. In addition, we propose an optimal reference frame selection algorithm for a better trade-off between the computational complexity and rendered image quality. Experimental results show that the proposed method can achieve a peak signal-to-noise ratio gain of 0.94 dB on average for multiview video test sequences when compared with the benchmark view synthesis reference software. In addition, the subjective quality of the rendered image is also improved.