Image Blocking Artifacts Reduction via Patch Clustering and Low-Rank Minimization

Transform coding is the most popular image compression method. However, for high compression it produces noticeable blocking artifacts in the decoded image. This paper presents new algorithms for reducing blocking artifacts. The algorithms use piecewise self-similarity within different parts of the image as a priori to give reasonable modification to the block boundary pixels. The proposed postprocessing algorithm includes block classification in transform coefficient domain, and each block boundary area smoothing is performed by searching similar blocks. In experiments it outperforms an existing method and gives significant subjective and objective improvement over the decoded image.

A smoothness constraint set based on local statistics of BDCT coefficients for image postprocessing

Weighted roll-off scheme to remove block artifacts for low power local dimming liquid crystal displays

In this paper, an efficient adaptive de-blocking algorithm is proposed to reduce blocking artifacts. Blocking artifacts are modeled as step functions and then the image blocks are divided into three categories: smooth blocks, texture blocks and edge blocks. For smooth blocks, the expression of amplitude of blocking artifacts is educed firstly in our algorithm, and then the adaptive smooth filter according to the amplitude of blocking artifacts and the smooth degree function is proposed to reduce blocking artifacts. For the texture blocks and edge blocks, the Sigma filter is used to smooth the block boundaries. The experiment results show that the proposed algorithm reduces the blocking artifacts effectively and preserves the original edges faithfully.

Based on regularization principles, we propose a new dequantization scheme for MPEG. The new approach drastically reduces blocking artifacts without smoothing the decoded image. Most discrete cosine transform (DCT) based video coding suffer from blocking artifacts where boundaries of 8/spl times/8 DCT blocks become visible on decoded images. The blocking artifacts become more prominent as the bit rate is lowered. We present a new dequantization technique for discrete cosine transform (DCT) based encoding to sharply reduce the blocking artifacts. Our proposed dequantization scheme, through regularization, sharply reduces blocking artifacts in decoded images. The performance of the proposed algorithm is evaluated against the standard MPEG decoding. The comparison shows visual improvements as well as numerical improvements in terms of the peak-signal-to-noise ratio (PSNR) and the blockiness measure (BM).

When a high compression ratio is required, the block discrete cosine transform (BDCT) decoded image suffers from the blocking effect: boundaries between adjacent image blocks visible for low bit rates. A blocking effect reduction algorithm based on DCT transform and Markov random field (MRF) is proposed to reduce blocking artifacts as many as possible, while preserving edge information. In this paper, the characteristic of human visual system (HVS) is sufficiently utilized. A visibility function of blocking artifacts is proposed based on the activity masking and brightness masking properties of HVS. The blocks which visibility of the blocking artifacts is less than the threshold do not need any processing. The proper threshold value for Huber function plays an important role in MRF method. The linear regression is used to adaptively estimate the threshold value. Experimental results show the proposed algorithm can significantly reduce blocking artifacts while preserving edge and texture information

We proposed the post-processing method to reduce the blocking artifact in low-bit-rate coded JPEG images, which is based on minimum block-boundary-discontinuity criterion. We analyzed the statistical property of block boundary pixel difference, classified all blocks into one of two types using OSLD (overlapped sub-laplacian distribution), and compensated the quantization error depending on the type of block, adaptively. Our postprocessing method can provide better visual quality as well as PSNR than any other method.

In this paper, we propose an efficient deblocking algorithm that uses block boundary characteristics and adaptive filtering in spatial domain. In the proposed algorithm, we detect the block boundary with blocking artifacts and classify the detected block boundaries into smooth regions or complex regions based on the statistical characteristics of neighborhood block. Thereafter, spatial adaptive filtering is performed. In smooth regions, the blocking artifacts seem a step-wise function, so the blocking artifacts can be reduced by a simple nonlinear 1-D 8-tap filter. In complex regions, there exist the blocking and ringing artifacts, so we propose adaptive filtering based on a feedforward neural network to reduce the blocking and ringing artifacts simultaneously. The horizontal and vertical block boundaries are processed separately with a different neural network. Experimental results show that the proposed algorithm gives better results than those of the conventional algorithms from both a subjective and objective viewpoints.

This paper presents a method to remove blocking artifacts in low bit-rate block-based video coding. The proposed algorithm has two separate filtering modes, which are selected by pixel behavior around the block boundary. In each mode, proper one-dimensional filtering operations are performed across the block boundary along the horizontal and vertical directions, respectively. In the first mode, corresponding to flat regions, a strong filter is applied inside the block as well as on the block boundary because the flat regions are more sensitive to the human visual system (HVS) and the artifacts propagated from the previous frame due to motion compensation are distributed inside the block. In the second mode, corresponding to other regions, a sophisticated smoothing filter which is based on the frequency information around block boundaries, is used to reduce blocking artifacts adaptively without introducing undesired blur. Even though the proposed deblocking filter is quite simple, it improves both subjective and objective image quality for various image features.

Coding of images or processing of images has played a momentous role in the success of digital communications and multimedia. Coding plays a major role is 3D mobile generation. The image processing methods have been exploited through the years to improve the quality of digital images. In the image processing system De-noising based coding is used extensively. As we know that, in low bit rate applications, the blocking artifacts problem arises, which severely reduce the visual quality of the image. Reducing blocking artifacts is essential to render the compressed visual data acceptable to the viewer. So, to detect and reduce noise from the image is a major task. So removing the noise from original signal is still a challenging problem for researchers. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. For real-time applications like television, photo-phone, etc. it is essential to reduce the noise power as much as possible and to retain the fine details and the edges in the image as well. Moreover, it is very important to have very low computational complexity so that the filtering operation is performed in a short time for online and real-time applications. The main focus of this paper is first to define an algorithm and based on that a non linear threshold filter is described which not only preserve the actual image structures in the presence of different types of noises (Salt & peepers, Speckle and Gaussian Noise) as well as it provide better PSNR, MSE, MAE, & Time Complexity than others Classical algorithms.

Fast Image Restoration for Reducing Block Artifacts Based on Adaptive Constrained Optimization

In this paper, we propose a simple, non iterative blocking artifacts reduction method for block discrete cosine transform (DCT) compressed images, using adaptive bilateral filter. Bilateral filter when applied on a input image, smooths out the blocking artifacts by weighted averaging of the pixel values without smoothing the edges. The proper selection of the bilateral filter parameters is very important and affects the filtering results significantly. We select the bilateral filter parameters optimally, through empirical study. The bilateral filter parameters are made adaptive to different decompressed images using different quantization tables. Our main contribution in this paper is to select the bilateral filter parameters optimally and adaptively through empirical study, in application to image deblocking. The proposed method shows highly encouraging results both objectively and subjectively, when compared to many state of the art image deblocking schemes including a recent method based on adaptive bilateral filter.

Blocking artifacts exist in images and video sequences compressed to low bit rates using block discrete cosine transform (DCT) compression standards. In order to reduce blocking artifacts, a novel DCT domain technique is presented in this paper. Firstly, a new FIR Wiener filter which exploits the dependence of neighboring DCT coefficients based on the linear minimum mean-square-error (LMMSE) criterion is proposed. Then we apply the new FIR Wiener filter twice in a dual adaptive filtering structure to restore each quantized DCT coefficient. In addition, an efficient parameter estimation method is proposed for the designed filter. Experimental results show that the performance of the proposed method is comparable to the state-of-the-art methods but has low computational complexity.

The reconstructed images after compression produce annoying blocking artifacts near block boundaries, particularly in low bit-rate image compression, because each block is transformed and quantized independently Detection of blocking artifact has an important role in the design of image coding systems. Increasing the bit rate to obtain better quality images is too costly. In this paper, a novel pixel-domain technique for image blocking artifact detection is presented. The algorithm detects the regions of the image which present visible blocking artifacts. The blocking artifact detection and reduction is presented as a post- processing in the image decoder. The algorithm first detects the direction of artifact through power spectrum estimation of the e pixel absolute difference (PAD) in both horizontal and vertical directions. The power spectrum of the PAD is estimated using the Maximum Entropy method (MEM) which is a nonparametric method to perform accurate power spectrum estimation. The reduction process is applied on the direction of artifacts only to improve the image quality. The key of the reduction process is based on the modified projection operator to give reasonable modifications for the block boundary pixels. The simulation results illustrate the performance of the proposed method for the JPEG decoded images.

Blocking artifacts are inherent and inevitable phenomena at low bit rates in current block-based video coding schemes. The paper proposes a deblocking technique to reduce blocking artifacts and improve visual quality. Different from the post-processing technique adopted in MPEG-4, the proposed technique is directly applied to the reference image for effective motion prediction instead of just for display purposes. In other words, the deblocking filter is introduced into the motion compensation loop in the video coding. Because of the negative effects of the deblocking filter, such as blurring the details of the image, not every block boundary needs to be filtered. It has to be determined whether the boundaries between two blocks should be filtered or not. Therefore, we also propose a decision-making criterion based on the available motion information (vector and mode). Experimental results show that the proposed deblocking method can improve image quality both in subjective and objective aspects. It also outperforms the post-processing technique in MPEG-4.