Reduce Blocking Artifacts Research Articles

Transform Domain-Based Perceptual Detection and Reduction of Blocking Artifacts

In this paper, provide a simple and effective method for measuring blocking artefacts with an ideal 2-D step function in this study. First, a basic edge detection technique for measuring blocking artefacts is proposed. The ideal 2-D step function is chosen based on the presence of blocking artefacts in the edge image. The blocking artefact reduction algorithm in frequency domain is designed to extract all of the parameters required to detect the presence of blocking artefacts and replace the optimal step function with a ramp function by replacing the coefficient of the first row of horizontal blocks with the coefficient of the shifted block. The proposed strategy was tested on various standard benchmark photos and found to increase the perceptual quality of JPEG compressed images after blocking artefact removal with the proposed method.

Adaptive rate image compressive sensing based on the hybrid sparsity estimation model

In some actual image compressive sensing (CS) systems, the sparsity of the original image signals is unknown on the sampling side. Resources on the sampling side are also limited. Therefore, it is a challenging task to implement adaptive rate allocation on the sampling side of a CS system. In this paper, a novel adaptive rate allocation block CS scheme for images based on a hybrid sparsity estimation model is proposed. First, a cosine similarity sparsity estimation model is constructed by directly modeling the measurement results, and on the reconstruction side, a thresholding cosine transform sparsity estimation model is constructed according to the initial reconstructed image. Then, the sparsity estimates generated by the above two models are fused according to their respective characteristics using a Bayesian probability fusion algorithm. The advantage of the proposed scheme is that it can significantly improve the accuracy of the final sparsity estimates while limiting the computational complexity on the sampling side, which makes it exceptionally suitable for wireless sensor network applications. On the reconstruction side, we propose an energy-based adaptive global reconstruction model, that can further reduce block artifacts caused by block-based CS. The experiment results show that, compared with previous state-of-the-art schemes, the proposed scheme can achieve a significant improvement in the peak signal-to-noise ratio at the same sampling rate.

Deep Wide-Activated Residual Network Based Joint Blocking and Color Bleeding Artifacts Reduction for 4:2:0 JPEG-Compressed Images

Blocking and color bleeding are two well-known artifacts for 4:2:0 JPEG-compressed images. Blocking mainly results from the block-level quantization of the luma component, while color bleeding is mainly caused by the subsampling and quantization of chroma components. Restoring luma can reduce blocking distortion, but with little influence on color bleeding. On the contrary, color bleeding can be removed via chroma components restoration. This letter proposes a deep wide-activated residual network for reducing blocking and color bleeding artifacts simultaneously, in which the luma and chroma components are jointly restored. Chroma components usually suffer from more severe distortion than the luma component due to subsampling and coarse quantization. Thus, we use the luma component to guide the restoration of chroma components. Moreover, we reduce blocking and color bleeding artifacts in low-resolution space via pixel shuffle-based decimation and assembling, which allows to obtain high restoration speed. Experimental results show that the proposed approach achieves state-of-the-art performance on joint blocking and color bleeding artifacts reduction.

Measurement Structures of Image Compressive Sensing for Green Internet of Things (IoT).

Image compressive sensing (CS) is a potential imaging scheme for green internet of things (IoT). To further make CS-based sensor adaptable to low bandwidth and low power, this paper focuses on finding a good measurement structure, i.e., the organization and storage format of CS measurements. Three potential measurement structures are proposed in this paper, respectively raster structure (RA), patch structure, and layer structure (LA). RA stores CS measurements of each column in an image, and PA packets CS measurements of overlapping patches forming an image. LA enables the measuring of small blocks and recovery of large blocks. All of the three structures avoid high computation complexity and huge memory in the process of measuring and recovery, and efficiently suppress the annoying blocking artifacts which often occur in traditional block structures. Experimental results show that RA, PA, and LA can efficiently reduce blocking artifacts, and produce comforting visual qualities. LA, especially, presents both good time-distortion and rate-distortion performance. By this paper, it is proved that LA is a suitable measurement structure for green IoT.

Wavelet transform based on Meyer algorithm for image edge and blocking artifact reduction

In this study, a wavelet transform based on Meyer algorithm with edge-angle tracking capability is proposed for edge and blocking artifact reduction of an image, during image compression processes. This new proposed scheme is quite different from the conventional curvelet scheme, which can be viewed as using post-processing, after wavelet transform, to track only the edge of a high-frequency section of wavelet coefficients. However, the proposed wavelet transform scheme based on Meyer algorithm is with a complete wavelet design procedure, which is suitable for multiresolution analysis; it possesses the orthonormal property of conventional wavelet transforms. From computer simulation, we verify the merits of the proposed scheme over the conventional approaches, in terms of achieving better performance based on the subjective and objective (e.g., the peak signal-to-noise ratio) analysis.

BLOCK ARTIFACT REDUCTION USING BINARY EDGE RETAINING MAPS

in this paper we propose a new edge-retaining algorithm for block artifact reduction. We have classified the pixels to categorize every pixel into one amongst two categories, particularly edge region and smooth region and then Edge-Retaining maps are used to describe these regions. Finally, A Two-step adaptive filter, based on these maps and incorporating offset filtering and edge retaining filtering is used for removing the block artifacts. Experimental Results prove that our proposed edge retaining algorithm adequately reduces block artifacts while conserving the details and the Peak to Signal Noise Ratio (PSNR) performance is better than that of various previous algorithms which use block classification.

Reducing Image Compression Artifacts by Structural Sparse Representation and Quantization Constraint Prior

The block discrete cosine transform (BDCT) has been widely used in current image and video coding standards, owing to its good energy compaction and decorrelation properties. However, because of independent quantization of DCT coefficients in each block, BDCT usually gives rise to visually annoying blocking compression artifacts, especially at low bit rates. In this paper, to reduce blocking artifacts and obtain high-quality images, image deblocking is cast as an optimization problem within maximum a posteriori framework, and a novel algorithm for image deblocking by using structural sparse representation (SSR) prior and quantization constraint (QC) prior is proposed. The SSR prior is utilized to simultaneously enforce the intrinsic local sparsity and the nonlocal self-similarity of natural images, while QC is explicitly incorporated to ensure a more reliable and robust estimation. A new split Bregman iteration-based method with an adaptively adjusted regularization parameter is developed to solve the proposed optimization problem, which makes the entire algorithm more practical. Experiments demonstrate that the proposed image-deblocking algorithm combining SSR and QC outperforms the current state-of-the-art methods in both peak signal-to-noise ratio and visual perception.

Digital Image Fusion Using HVS in Block Based Transforms

The main aim of image fusion is to integrate the qualitative visual information from multiple images into a single image. Image fusion is implemented in spatial and transform domains. The implementation of algorithm in spatial domain is simple. But, the images are stored/transmitted using popular methods like JPEG and JPEG2000, which are implemented in the transform domain. Therefore fusion algorithms in spatial domain are not suitable for real time application. Image transforms are categorized as block-based and multi resolution-based transforms. In this study, block-based transforms such as Hadamard Transform (HT), Discrete Cosine Transform (DCT), Haar Transform (HrT), and Slant Transform (ST) are considered for image fusion. The DCT based approaches are suffering from undesirable side effects such as blurring and blocking artifacts that reduce the quality of the fused image. In this paper, the Human Visual System (HVS) model is considered to select the appropriate block from multiple images to obtain the fused image. The proposed approach is applied to all the block-based transforms to assess the performance. Methods such as Mutual Information (MI), Edge Strength and Orientation Preservation (ESOP), Feature Similarity Index (FSIM), Normalized Cross Correlation (NCC) and Score are used to assess the performance of the proposed algorithms. The experimental results indicate that the proposed method is better in terms of improved quality and reduced blocking artifacts.

Reduction of Blocking Artifact in JPEG Images with Orthogonal Polynomials

Reduction of Blocking Artifact in JPEG Images with Orthogonal Polynomials

Image compression algorithm with reduced blocking artifacts

Image compression algorithm with reduced blocking artifacts

Random Walk Graph Laplacian-Based Smoothness Prior for Soft Decoding of JPEG Images.

Given the prevalence of joint photographic experts group (JPEG) compressed images, optimizing image reconstruction from the compressed format remains an important problem. Instead of simply reconstructing a pixel block from the centers of indexed discrete cosine transform (DCT) coefficient quantization bins (hard decoding), soft decoding reconstructs a block by selecting appropriate coefficient values within the indexed bins with the help of signal priors. The challenge thus lies in how to define suitable priors and apply them effectively. In this paper, we combine three image priors-Laplacian prior for DCT coefficients, sparsity prior, and graph-signal smoothness prior for image patches-to construct an efficient JPEG soft decoding algorithm. Specifically, we first use the Laplacian prior to compute a minimum mean square error initial solution for each code block. Next, we show that while the sparsity prior can reduce block artifacts, limiting the size of the overcomplete dictionary (to lower computation) would lead to poor recovery of high DCT frequencies. To alleviate this problem, we design a new graph-signal smoothness prior (desired signal has mainly low graph frequencies) based on the left eigenvectors of the random walk graph Laplacian matrix (LERaG). Compared with the previous graph-signal smoothness priors, LERaG has desirable image filtering properties with low computation overhead. We demonstrate how LERaG can facilitate recovery of high DCT frequencies of a piecewise smooth signal via an interpretation of low graph frequency components as relaxed solutions to normalized cut in spectral clustering. Finally, we construct a soft decoding algorithm using the three signal priors with appropriate prior weights. Experimental results show that our proposal outperforms the state-of-the-art soft decoding algorithms in both objective and subjective evaluations noticeably.

An Efficient Deblocking Filter Algorithm for Reduction of Blocking Artifacts in HEVC Standard

An Efficient Deblocking Filter Algorithm for Reduction of Blocking Artifacts in HEVC Standard

Beyond Low Rank + Sparse: Multiscale Low Rank Matrix Decomposition

We present a natural generalization of the recent low rank + sparse matrix decomposition and consider the decomposition of matrices into components of multiple scales. Such decomposition is well motivated in practice as data matrices often exhibit local correlations in multiple scales. Concretely, we propose a multi-scale low rank modeling that represents a data matrix as a sum of block-wise low rank matrices with increasing scales of block sizes. We then consider the inverse problem of decomposing the data matrix into its multi-scale low rank components and approach the problem via a convex formulation. Theoretically, we show that under various incoherence conditions, the convex program recovers the multi-scale low rank components either exactly or approximately. Practically, we provide guidance on selecting the regularization parameters and incorporate cycle spinning to reduce blocking artifacts. Experimentally, we show that the multi-scale low rank decomposition provides a more intuitive decomposition than conventional low rank methods and demonstrate its effectiveness in four applications, including illumination normalization for face images, motion separation for surveillance videos, multi-scale modeling of the dynamic contrast enhanced magnetic resonance imaging and collaborative filtering exploiting age information.

CONCOLOR: Constrained Non-Convex Low-Rank Model for Image Deblocking.

Due to independent and coarse quantization of transform coefficients in each block, block-based transform coding usually introduces visually annoying blocking artifacts at low bitrates, which greatly prevents further bit reduction. To alleviate the conflict between bit reduction and quality preservation, deblocking as a post-processing strategy is an attractive and promising solution without changing existing codec. In this paper, in order to reduce blocking artifacts and obtain high-quality image, image deblocking is formulated as an optimization problem within maximum a posteriori framework, and a novel algorithm for image deblocking using constrained non-convex low-rank model is proposed. The ℓ(p) (0 < p < 1) penalty function is extended on singular values of a matrix to characterize low-rank prior model rather than the nuclear norm, while the quantization constraint is explicitly transformed into the feasible solution space to constrain the non-convex low-rank optimization. Moreover, a new quantization noise model is developed, and an alternatively minimizing strategy with adaptive parameter adjustment is developed to solve the proposed optimization problem. This parameter-free advantage enables the whole algorithm more attractive and practical. Experiments demonstrate that the proposed image deblocking algorithm outperforms the current state-of-the-art methods in both the objective quality and the perceptual quality.

적응적인 PU 기반 움직임 벡터 외삽과 경계 정합을 통한 프레임 전체 오류 은닉 방법에 관한 연구

,-./0123 4256789:; 4?@A. BCD789:; PBQ>RSN46TUV (Error Concealment : EC) WXYZOIA. OIB6WXYZ46High Efficiency Video Coding (HEVC) E4[\]#^=(_`abQ 1 (Adaptive Prediction Unit-based Motion Vector Extrapolation : APMVE)c\]#^=(_(Boundary Matching : BM) WXYZ(_A. APMVE6[\]#^= dUef ( 1`abQ(Prediction Unit : PU)=(_Bg RbQ(Error Concealment Basic Unit : ECBU)=dUPBXBM c\]#^=(_Bg2hijk(Unreliable Block)lBgmnA. OIB6WXYZ oWXYZ4pqp6jkrst=ud UOvBgw x#tyzg+A.AbstractRecently, most of the video services are usually transmitted in wireless networks. In networks environment, a packet of video is likely to be lost during transmission. For this reason, this paper proposes a new Error Concealment (EC) algorithm. For High Efficiency Video Coding (HEVC) bitstreams, the proposed algorithm includes Adaptive Prediction Unit-based Motion Vector Extrapolation (APMVE) and Boundary Matching (BM) algorithm, which employs both the temporal and spatial correlation. APMVE adaptively decides a Error Concealment Basic Unit (ECBU) by using the PU information of the previous frame and BM employing the spatial correlation is applied to only unreliable blocks. Simulation results show that the proposed algorithm provides the higher subjective quality by reducing blocking artifacts which appear in other existing algorithms.Keyword : Error Concealment, Boundary Matching, Motion Vector Extrapolation, HEVC, Prediction Unit

Efficient robust filtering technique for blocking artifacts reduction

This paper presents a new post-processing algorithm based on a robust statistical model to remove the blocking artifacts observed in block discrete cosine transform (BDCT)-based image compression standards. The novelty is the implementation of a new robust weight function for the block artifact reduction. The blocking artifacts in an image are treated as an outlier random variable. The robust formulation aims at eliminating the artifacts outliers, while preserving the edge structures in the restored image. Extensive simulation results and comparative studies demonstrate that the presented method provides superior results in terms of pixel-wise (PSNR) and perceptual (SSIM) measures.

Optimization method to reduce blocking artifacts in JPEG images

This paper presents an optimization method to reduce blocking artifacts in JPEG images by utilizing the image gradient information. A closed-form solution is derived for the optimization method. To address the computational feasibility aspect of the large matrices involved in the closed-form solution, a sliding window approach is devised. The performance of the developed method is compared with several blocking artifacts reduction methods in the literature and also with the deblocking filter deployed in high efficiency video coding by examining the three measures of peak signal-to-noise ratio, generalized block-edge impairment metric (MGBIM), and structural similarity. The comparison results indicate the effectiveness of the introduced method in particular for low bit-rate JPEG images.

Post-Processing for Blocking Artifact Reduction Based on Inter-Block Correlation

Block-based coding introduces an undesirable discontinuity between neighboring blocks in reconstructed images. This image degradation, referred to as blocking artifacts, arises mainly due to the loss of inter-block correlation in the quantization process of discrete cosine transform coefficients. In many multimedia broadcasting applications, such as a television, decoded video sequences suffer from blocking artifacts. In this paper, we present a novel post-processing algorithm based on increment of inter-block correlation aimed at reducing blocking artifacts. We first smooth the three lowest frequency discrete cosine transform (DCT) coefficients between neighboring blocks, in order to reduce blocking artifacts in the flat region, which are most sensitive to the human visual system. We then group each edge block and its matched blocks together and apply group-based filtering to increase the correlation between grouped blocks. This suppresses blocking artifacts in the edge region while preserving details. In addition, the algorithm is extended to reduce flickering artifacts as well as blocking artifacts in video sequences. Experimental results show that the proposed method successfully alleviates blocking artifacts in both images and videos coded with low bit-rates.

Remote Sensing Image Resolution Enlargement Algorithm Based on Wavelet Transformation

In this paper, we present a new image resolution enhancement algorithm based on cycle spinning and stationary wavelet subband padding. The proposed technique or algorithm uses stationary wavelet transformation (SWT) to decompose the low resolution (LR) image into frequency subbands. All these frequency subbands are interpolated using either bicubic or lanczos interpolation, and these interpolated subbands are put into inverse SWT process for generating intermediate high resolution (HR) image. Finally, cycle spinning (CS) is applied on this intermediate high resolution image for reducing blocking artifacts, followed by, traditional Laplacian sharpening filter is used to make the generated high resolution image sharper. This new technique has been tested on several satellite images. Experimental result shows that the proposed technique outperforms the conventional and the state-of-the-art techniques in terms of peak signal to noise ratio, root mean square error, entropy, as well as, visual perspective.

New Frame Rate Up-Conversion Algorithms With Low Computational Complexity

This paper proposes a new frame rate up-conversion (FRUC) algorithm to reduce the computational complexity and to improve the peak signal-to-noise ratio (PSNR) performance. The proposed FRUC algorithm includes prediction-based motion vector smoothing (PMOS), partial average-based motion compensation (PAMC), and intrapredicted hole interpolation (IPHI). PMVS can efficiently remove outliers using motion vectors of neighboring blocks and PAMC performs motion compensation with the region-based partial average to reduce blocking artifacts of the interpolated frames. For hole interpolation, IPHI uses intraprediction of H.264/AVC to eliminate blurring and also uses the fixed weights implemented using only shift operations, which result in low computational complexity. Compared to the existing algorithms, which use bilateral motion estimation, the proposed algorithm improves the average PSNR of the interpolated frames by 3.44 dB and lowers PSNR performance only by 0.13 dB than the existing algorithm that employs unilateral ME; however, it can significantly reduce the computational complexity of FRUC about 89.3% based on the absolute difference.