A new image quality assessment method to detect and measure strength of blocking artifacts

Block based transform coding is one of the most popular techniques for image and video compression. However it suffers from several visual quality degradation factors, most notably from blocking artifacts. The subjective picture quality degradation caused by blocking artifacts, in general, does not agree well with the popular objective quality measure such as PSNR.A new image quality assessment method that detects and measures strength of blocking artifacts for block based transform coded images is proposed. In order to characterize the blocking artifacts, we utilize two observations: if blocking artifacts occur on the block boundary, the pixel value changes abruptly across the boundary and the same pixel values usually span along the entire length of the boundary. The proposed method operates only on a single block boundary to detect blocking artifacts. When a boundary is classified as having blocking artifacts, corresponding blocking artifact strength is also computed. Average values of those blocking artifact strengths are converted into a single number representing the subjective image quality. Experiments on various JPEG compressed images with various bit rates demonstrated that the proposed blocking artifacts measuring value matches well with the subjective image quality judged by human observers.     

一种基于人类视觉系统的去块效应算法

基于块离散余弦变换的图像和视频压缩主要缺点就是在低比特率时会在块边界出现明显的方块效应。本文提出一种充分利用人类视觉特性,在图像的平滑区和纹理区分别采用一维DCT域滤波和空间域滤波的去块效应算法。实验结果表明该算法既能有效地去除方块效应又能保护图像的边缘信息。     

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.     

采用超分辨率重建提升压缩图像质量的方法

针对JPEG的中低码率压缩图像即高压缩率图像存在较严重的块效应以及量化噪声,提出了一种对JPEG标准压缩图像进行优化的重建-采样方法.该方法对JPEG压缩图像采用三维块匹配算法(BM3D)进行去噪,去除图像中存在的块效应和量化噪声,进而提高超分辨率重建的映射准确性,再使用外部库对去噪后图像进行基于稀疏表示的超分辨率重建,补充一定的高频信息,最后对重建后的高分辨率图进行双三次下采样,得到与原始图像大小一致的图像作为最终优化图像.实验结果表明,该方法在中低码率情况下能够有效地提高JPEG压缩图像的质量,对高码率压缩图像也有一定效果.     

Undersampled boundary pre-/postfilters for low bit-rate DCT-based block coders.

It has been well established that critically sampled boundary pre-/postfiltering operators can improve the coding efficiency and mitigate blocking artifacts in traditional discrete cosine transform-based block coders at low bit rates. In these systems, both the prefilter and the postfilter are square matrices. This paper proposes to use undersampled boundary pre- and postfiltering modules, where the pre-/postfilters are rectangular matrices. Specifically, the prefilter is a "fat" matrix, while the postfilter is a "tall" one. In this way, the size of the prefiltered image is smaller than that of the original input image, which leads to improved compression performance and reduced computational complexities at low bit rates. The design and VLSI-friendly implementation of the undersampled pre-/postfilters are derived. Their relations to lapped transforms and filter banks are also presented. Two design examples are also included to demonstrate the validity of the theory. Furthermore, image coding results indicate that the proposed undersampled pre-/postfiltering systems yield excellent and stable performance in low bit-rate image coding.     

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。     

Image postprocessing by Non-local Kuan’s filter

Blocking artifacts exist in images and video sequences compressed to low bit rates using block-based discrete cosine transform (DCT) compression standards. In order to reduce blocking artifacts, two image postprocessing techniques, DNLK filter and OCDNLK filter, are presented in this paper. A more accurate DCT domain Kuan’s filter based on Non-local parameter estimation was proposed from the linear minimum mean-square-error (MMSE) criterion. We analyze the required two assumptions for the filter theoretically. Then the DCT domain Kuan’s filter for low frequency coefficients and Non-local mean filter for high frequency AC coefficients constitute the proposed Non-local Kuan’s (NLK) filter. After that, we propose the Dual Non-local Kuan’s (DNLK) filter by applying the proposed filter in dual layer. The DNLK filter is extended to form the Overcomplete Dual Non-local Kuan’s (OCDNLK) filter by applying to the overcomplete DCT coefficients. Experimental results on coded images using test quantization tables and JPEG coded images show the effectiveness of the two methods.     

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.     

Improved decoder for transform coding with application to the JPEGbaseline system

Transform coding, a simple yet efficient image coding technique, has been adopted by the Joint Photographic Experts Group (JPEG) as the basis for an emerging coding standard for compression of still images. However, for any given transform encoder, the conventional inverse transform decoder is suboptimal. Better performance can be obtained by a nonlinear interpolative decoder that performs table lookups to reconstruct the image blocks from the code indexes. Each received code index of an image block addresses a particular codebook to fetch a component vector. The image block can be reconstructed as the sum of the component vectors for that block. An iterative algorithm for designing a set of locally optimal codebooks is developed. Computer simulation results demonstrate that this improved decoding technique can be applied in the JPEG baseline system to decode enhanced quality pictures from the bit stream generated by the standard encoding scheme     

Image and video compression algorithms based on recovery techniquesusing mean field annealing

Image and video coding algorithms have found a number of applications ranging from video telephony on the public switched telephone networks (PSTN) to HDTV. However, as the bit rate is lowered, most of the existing techniques, as well as current standards, such as JPEG, H. 261, and MPEG-1 produce highly visible degradations in the reconstructed images primarily due to the information loss caused by the quantization process. In this paper, we propose an iterative technique to reduce the unwanted degradations, such as blocking and mosquito artifacts while keeping the necessary detail present in the original image. The proposed technique makes use of a priori information about the original image through a nonstationary Gauss-Markov model. Utilizing this model, a maximum a posteriori (MAP) estimate is obtained iteratively using mean field annealing. The fidelity to the data is preserved by projecting the image onto a constraint set defined by the quantizer at each iteration. The proposed solution represents an implementation of a paradigm we advocate, according to which the decoder is not simply undoing the operations performed by the encoder, but instead it solves an estimation problem based on the available bitstream and any prior knowledge about the source image. The performance of the proposed algorithm was tested on a JPEG, as well as on an H.261-type video codec. It is shown to be effective in removing the coding artifacts present in low bit rate compression     

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...     

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.     

Transform coded image reconstruction exploiting interblockcorrelation

Transmission of still images and video over lossy packet networks presents a reconstruction problem at the decoder. Specifically, in the case of block-based transform coded images, loss of one or more packets due to network congestion or transmission errors can result in errant or entirely lost blocks in the decoded image. This article proposes a computationally efficient technique for reconstruction of lost transform coefficients at the decoder that takes advantage of the correlation between transformed blocks of the image. Lost coefficients are linearly interpolated from the same coefficients in adjacent blocks subject to a squared edge error criterion, and the resulting reconstructed coefficients minimize blocking artifacts in the image while providing visually pleasing reconstructions. The required computational expense at the decoder per reconstructed block is less than 1.2 times a non-recursive DCT, and as such this technique is useful for low power, low complexity applications that require good visual performance.     

Biorthogonal and nonuniform lapped transforms for transform codingwith reduced blocking and ringing artifacts

New lapped transforms are introduced. The lapped biorthononal transform (LBT) and hierarchical lapped biorthogonal transform (HLBT) are appropriate for image coding, and the modulated HLBT biorthogonal transform (MMLBT) and nonuniform modulated lapped biorthogonal transform (NMLBT) are appropriate for audio coding. The HLBT has a significantly lower computational complexity than the lapped orthogonal transform (LOT), essentially no blocking artifacts, and fewer ringing artifacts than the commonly used discrete cosine transform (DCT). The LBT and HLBT have transform coding gains that are typically between 0.5 and 1.2 dB higher than that of the DCT. Image coding examples using JPEG and embedded zerotree coders demonstrate the better performance of the LET and HLBT. The NMLBT has fewer ringing artifacts and better reproduction of transient sounds than the MLT, as shown in audio coding examples. Fast algorithms for both the HLBT and the NMLBT are presented     

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.     

Down-scaling for better transform compression

The most popular lossy image compression method used on the Internet is the JPEG standard. JPEG's good compression performance and low computational and memory complexity make it an attractive method for natural image compression. Nevertheless, as we go to low bit rates that imply lower quality, JPEG introduces disturbing artifacts. It is known that, at low bit rates, a down-sampled image, when JPEG compressed, visually beats the high resolution image compressed via JPEG to be represented by the same number of bits. Motivated by this idea, we show how down-sampling an image to a low resolution, then using JPEG at the lower resolution, and subsequently interpolating the result to the original resolution can improve the overall PSNR performance of the compression process. We give an analytical model and a numerical analysis of the down-sampling, compression and up-sampling process, that makes explicit the possible quality/compression trade-offs. We show that the image auto-correlation can provide a good estimate for establishing the down-sampling factor that achieves optimal performance. Given a specific budget of bits, we determine the down-sampling factor necessary to get the best possible recovered image in terms of PSNR.     

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.     

Restoration of lossy compressed noisy images

Noise degrades the performance of any image compression algorithm. However, at very low bit rates, image coders effectively filter noise that may he present in the image, thus, enabling the coder to operate closer to the noise free case. Unfortunately, at these low bit rates the quality of the compressed image is reduced and very distinctive coding artifacts occur. This paper proposes a combined restoration of the compressed image from both the artifacts introduced by the coder along with the additive noise. The proposed approach is applied to images corrupted by data-dependent Poisson noise and to images corrupted by film-grain noise when compressed using a block transform-coder such as JPEG. This approach has proved to be effective in terms of visual quality and peak signal-to-noise ratio (PSNR) when tested on simulated and real images.     

基于骑士巡游的灰度图象加密压缩算法

为了进一步提高涉密图像的存储和传输效率,提出了基于骑士巡游的灰度图像加密压缩算法。该算法将原始图像进行8×8分块,对每个块进行DCT变换,构建以块为单位的系数块化矩阵,然后采用骑士巡游置乱对该系数块化矩阵加密,对置乱加密后图像进行JPEG压缩得到加密压缩图像。实验仿真和分析表明,该算法密钥空间大、密钥敏感性高,在保证一定置乱度的前提下,获得了更高的压缩效率。     

Medical image compression by discrete cosine transform spectralsimilarity strategy

Due to bandwidth and storage limitations, medical images must be compressed before transmission and storage. However, the compression reduces the image fidelity, especially when the images are compressed at low bit rates. The reconstructed images suffer from blocking artifacts and the image quality is severely degraded under high compression ratios. In this paper, we present a strategy to increase the compression ratio with low computational burden and excellent decoded quality. We regard the discrete cosine transform as a bandpass filter to decompose a sub-block into equal-sized bands. After a band-gathering operation, a high similarity property among the bands is found. By utilizing the similarity property, the bit rate of compression can be greatly reduced. Meanwhile, the characteristics of the original image are not sacrificed. Thus, it can avoid the misdiagnosis of diseases. Simulations were carried out on different kinds of medical images to demonstrate that the proposed method achieves better performance when compared to other existing transform coding schemes, such as JPEG, in terms of bit rate and quality. For the case of angiogram images, the peak signal-to-noise-ratio gain is 13.5 dB at the same bit rate of 0.15 bits per pixel when compared to the JPEG compression. As for the other kinds of medical images, their benefits are not so obvious as for angiogram images; however, the gains for them are still 4-8 dB at high compression ratios. Two doctors were invited to verify the decoded image quality; the diagnoses of all the test images were correct when the compression ratios were below 20