Deblocking Method Research Articles

Deep steerable pyramid wavelet network for unified JPEG compression artifact reduction

Although numerous methods have been proposed to remove blocking artifacts in JPEG-compressed images, one important issue not well addressed so far is the construction of a unified model that requires no prior knowledge of the JPEG encoding parameters to operate effectively on different compression-level images (grayscale/color) while occupying relatively small storage space to save and run. To address this issue, in this paper, we present a unified JPEG compression artifact reduction model called DSPW-Net, which employs (1) the deep steerable pyramid wavelet transform network for Y-channel restoration, and (2) the classic U-Net architecture for CbCr-channel restoration. To enable our model to work effectively on images with a wide range of compression levels, the quality factor (QF) related features extracted by the convolutional layers in the QF-estimation network are incorporated in the two restoration branches. Meanwhile, recursive blocks with shared parameters are utilized to drastically reduce model parameters and shared-source residual learning is employed to avoid the gradient vanishing/explosion problem in training. Extensive quantitative and qualitative results tested on various benchmark datasets demonstrate the effectiveness of our model as compared with other state-of-the-art deblocking methods.

An efficient post-processing adaptive filtering technique to rectifying the flickering effects.

Compression at a very low bit rate(≤0.5bpp) causes degradation in video frames with standard decoding algorithms like H.261, H.262, H.264, and MPEG-1 and MPEG-4, which itself produces lots of artifacts. This paper focuses on an efficient pre-and post-processing technique (PP-AFT) to address and rectify the problems of quantization error, ringing, blocking artifact, and flickering effect, which significantly degrade the visual quality of video frames. The PP-AFT method differentiates the blocked images or frames using activity function into different regions and developed adaptive filters as per the classified region. The designed process also introduces an adaptive flicker extraction and removal method and a 2-D filter to remove ringing effects in edge regions. The PP-AFT technique is implemented on various videos, and results are compared with different existing techniques using performance metrics like PSNR-B, MSSIM, and GBIM. Simulation results show significant improvement in the subjective quality of different video frames. The proposed method outperforms state-of-the-art de-blocking methods in terms of PSNR-B with average value lying between (0.7–1.9db) while (35.83–47.7%) reduced average GBIM keeping MSSIM values very close to the original sequence statistically 0.978.

Enhancement of Facilitation Training for Aphasia by Transcranial Direct Current Stimulation

We aimed to enhance the performance of naming and sentence production in chronic post-stroke aphasia by tablet-based language training combined with transcranial direct current stimulation (tDCS) conducted on non-consecutive days. We applied a deblocking method involved in stimulation–facilitation therapy to six participants with chronic aphasia who performed naming and sentence production tasks for impaired modalities, immediately after a spoken-word picture-matching task for an intact modality. The participants took part in two conditional sessions: a tDCS condition in which they performed a spoken word-picture matching task while we delivered an anodal tDCS over the left inferior frontal cortex; and a sham condition in which sham stimulation was delivered. We hypothesized that, compared with the sham stimulation, the application of anodal tDCS over the left inferior frontal cortex during the performance of tasks requiring access to semantic representations would enhance the deblocking effect, thereby improving the performances for subsequent naming and sentence production. Our results showed greater improvements 2 weeks after training with tDCS than those after training with sham stimulation. The accuracy rate of naming was significantly higher in the tDCS condition than in the sham condition, regardless of whether the words were trained or not. Also, we found a significant improvement in the production of related words and sentences for the untrained words in the tDCS condition, compared with that found pre-training, while in the sham condition we found no significant improvement compared with that found pre-training. These results support our hypothesis and suggest the effectiveness of the use of tDCS during language training on non-consecutive days.

EVALUATION OF DEBLOCKING METHODS FOR CHANG’E-4 DESCENT IMAGES

Abstract. Chang’e-4 lunar probe has successfully landed on the far side of the moon in Von Kármán crater inside the South Pole-Aitken (SPA) basin at 10:26 am on January 3, 2019. Due to the reduction of the coding rate, obvious block effects appear at the boundaries of descent images. Unblock, adaptive fast bilateral filtering, structure-texture enhancement and high-order Markov random field methods, are applied to remove the block effect of the descent images. Based on analysing the quality of descent images, quantitative comparison of four methods is performed using simulated compressed 1:64 descent images and real images. Comprehensive analysis was performed using typical measures such as PSNR, SSIM and NIQE. Experimental results show that adaptive fast bilateral filtering is better than other methods. The deblocked 1:64 image sequences have been used to assist localizing the landing point quickly during the mission.

Deep‐network based method for joint image deblocking and super‐resolution

Many pieces of research have been conducted on image-restoration techniques to recover high-quality images from their low-quality versions, but they usually aim to handle a single degraded factor. However, captured images usually suffer from various degradation factors, such as low resolution and compression distortion, in the procedures of image acquisition, compression, and transmission simultaneously. Ignoring the correlation of different degraded factors may result in the limited efficiency of the existing image-restoration methods for captured images. A joint deep-network-based image-restoration algorithm is proposed to establish a restoration framework for image deblocking and super-resolution. The proposed convolutional neural network is made up of two stages. A deblocking network is constructed with two cascade deblocking subnets first, then, super-resolution is performed by a very deep network with skipping links. Cascading these two stages forms a novel deep network. An end-to-end training scheme is developed, which makes the two stages be trained jointly so as to achieve better performance. Intensive evaluations have been conducted to measure the performance of the authors’ method both in general images and face images. Experimental results on several datasets demonstrate that the proposed method outperforms other state-of-the-art methods, in terms of both subjective and objective performances.

Image Deblocking Detection Based on a Convolutional Neural Network

With the development of multimedia processing technology, it is becoming much easier to manipulate and tamper with digital video without leaving any visual clues. Because video compression is very common in digital videos, the tamper might employ powerful multimedia deblocking methods to cover up the video tampering traces. Motion JPEG (MJPEG) is one of the most popular video formats, in which each video frame or interlaced field of a digital video sequence is compressed separately as a JPEG image. By splitting the MJPEG video into JPEG image frames, the tamper might employ powerful multimedia deblocking methods to cover up the video tampering traces. To the best our knowledge, there is no existing method for the forensics of deblocking. In this paper, we propose a novel method to detect deblocking, which can automatically learn feature representations based on a deep learning framework. We first train a supervised convolutional neural network (CNN) to learn the hierarchical features of deblocking operations with labeled patches from the training datasets. The first convolutional layer of the CNN serves as the preprocessing module to efficiently obtain the tampering artifacts. Then, we extract the features for an image with the CNN on the basis of a patch by applying a patch-sized sliding-window to scan the whole image. The generated image representation is then condensed by a simple feature fusion technique, i.e., regional pooling, to obtain the final discriminative feature. The experimental results on several public datasets demonstrate the superiority of the proposed scheme.

Image deblocking via joint domain learning

Image deblocking is a postprocessing method that aims to suppress the compression artifacts without changing existing JPEG coding standard. We propose an image deblocking method, which is based on deep convolutional neural networks. The proposed method takes full advantage of the characteristics of wavelet domain and pixel domain to restore the high-frequency information of compressed images and maintain low-frequency information, respectively. In addition, a fusion layer is employed to fuse the merits of two domains. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art deblocking methods in both subjective vision and objective evaluation.

A multi-model restoration algorithm for recovering blood vessels in skin images

Blood vessels under skin surface have been used as a biometric trait for many years. Traditionally, they are used only in commercial and governmental applications because infrared images are required to capture high quality blood vessels. Recent research results demonstrate that blood vessels can be extracted directly from color images potentially for forensic applications. However, color images taken by consumer cameras are likely compressed by the JPEG compression method. As a result, the quality of the color images is seriously degraded, which makes the blood vessels difficult to be visualized. In this paper, a multi-model restoration algorithm (MMRA) is presented to remove blocking artifacts in JPEG compressed images and restore the lost information. Two mathematical properties in the JPEG compression process are identified and used to design MMRA. MMRA is based on a tailor-made clustering scheme to group training data and learns a model, which predicts original discrete cosine transform coefficients, from each grouped dataset. An open skin image database containing 978 forearm images and 916 thigh images with weak blood vessel information and a set of diverse skin images collected from the Internet are used to evaluate MMRA. Different resolutions and different compression factors are examined. The experimental results show clearly that MMRA restores blood vessels more effectively than the state-of-the-art deblocking methods.

Variational JPEG artifacts suppression based on high-order MRFs

The Block-wise Discrete Cosine Transform (B-DCT) based compression technique has been widely used in image and video coding standards. However, at high compression ratios, the coded images inevitably contain annoying blocking artifacts. In this paper, the author proposes a novel variational model for blocking artifacts suppression, which combines a discriminatively trained Fields of Experts (FoE) image prior model and the indicator function of the quantization constraint set (QCS). The FoE prior model is a filter-based higher-order Markov Random Fields (MRF) model, and it has proven to be effective for many image restoration problems. The resulting variational model leads to a generally difficult non-convex optimization problem, which can be efficiently solved by a recently proposed non-convex optimization algorithm. Numerical experiments show that the proposed deblocking approach leads to visually strongly comparable performance to state-of-the-art deblocking methods across a range of compression levels. Furthermore, our method can achieve higher PSNR-B results, which is a block-sensitive index, specialized for deblocked image evaluation and correlates well with subjective quality. Besides, the proposed model comes along with the additional advantage of high efficiency.

A Solution to the Common Problem of the Synthesis and Applications of Hexachlorofluorescein Labeled Oligonucleotides.

A common problem of the preparation of hexachlorofluorescein labeled oligonucleotides is the transformation of the fluorophore to an arylacridine derivative under standard ammonolysis conditions. We show here that the arylacridine byproduct with distinct optical characteristics cannot be efficiently separated from the major product by HPLC or electrophoretic methods, which hampers precise physicochemical experiments with the labeled oligonucleotides. Studies of the transformation mechanism allowed us to select optimal conditions for avoiding the side reaction. The novel method for the post-synthetic deblocking of hexachlorofluorescein-labeled oligodeoxyribonucleotides described in this paper prevents the formation of the arylacridine derivative, enhances the yield of target oligomers, and allows them to be proper real-time PCR probes.

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.

Curvelet transform based adaptive image deblocking method

Blocking effect caused by coefficient quantification during image compression is an annoying problem. The main effect of quantification is to eliminate high frequency component in the image. Therefore, it will lead to noticeable discontinuous leaps which causes block effect and degrade the image. A deblocking method based on Curvelet transform is proposed in this paper. Based on the fact that the Curvelet coefficients in different scale layers correspond to different block effect of the degraded image, our method adaptively process the coefficients in each layer to recover the degraded images. As proved by the experiments, our method can retain more details and get better recovery results under both subjective and objective criterions than traditional spatial domain and wavelet deblocking methods.

A generic post-deblocking filter for block based image compression algorithms

In this paper we propose a new post-processing deblocking technique that is independent of the compression method used to encode the image. The development of this filter was motivated by the use of Multidimensional Multiscale Parser (MMP) algorithm, a generic lossy and lossless compression method. Since it employs an adaptive block size, it presents some impairments when using the deblocking techniques presented in the literature. This led us to the development of a new and more generic deblocking method, based on total variation and adaptive bilateral filtering.The proposed method was evaluated not only for still images, but also for video sequences, encoded using pattern matching and transform based compression methods. For all cases, both the objective and subjective quality of the reconstructed images were improved, showing the versatility of the proposed technique.

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.

Content-adaptive deblocking for high efficiency video coding

In this paper, a content-adaptive deblocking method is proposed to improve the visual quality of block-based DCT compressed videos. We find that the edge information obtained through the global orientation energy edge detection (OEED) on an initially deblocked image provides a robust partition of local directional features (LDFs). Based on this partition, for the directional featured region, we design corresponding filter orientation and thresholds to best preserve image details while reducing blocking artifacts; for the consecutive non-featured region, we impose extra smoothing to suppress the visually severe blocking artifacts. Experimental results demonstrate that our method effectively improves the visual quality while well maintaining the objective fidelity of block-based DCT compressed videos, compared with the H.264/AVC deblocking method and other existing directional deblocking methods.

Reducing blocking artifacts in JPEG-compressed images using an adaptive neural network-based algorithm

It is well known that at low bit rates, a block-based discrete cosine transform compressed image or video can exhibit visually annoying blocking and ringing artifacts. Low-pass filters are very effective in reducing the blocking artifacts in smooth areas. However, it is difficult to achieve a satisfactory result for ringing artifact removal using only an adaptive filtering scheme. This paper presents a neural network-based deblocking method that is effective on various types of images. The first step of this scheme is block classification that identifies each 8 × 8 block as one of the three types: PLAIN, EDGE or TEXTURE, based on its statistical characteristics. The next step is the reduction in the blocking and ringing artifacts by applying three trained layered neural networks to three different types of image areas. Comparing this method with other algorithms, the simulation results clearly show that the proposed algorithm is very powerful in effectively reducing both blocking and ringing artifacts while preserving the true edge and textural information and thus significantly improving the visual quality of the blocking images or videos.

Detection method and filters for blocking effect reduction of highly compressed images

Image compression plays a pivotal role in minimizing the data size and reduction in transmission costs. Many coding techniques have been developed, but the most effective is the JPEG compression. However, the reconstructed images from JPEG compression produce noticeable image degradations near block boundaries called blocking artifacts, particularly in highly compressed images. A method to detect and reduce these artifacts without smoothing images and without removing perceptual features has been presented in this paper. In this work, a low computational deblocking filter with four modes is proposed, including three frequency-related modes (smooth, non-smooth, and intermediate) and a corner mode for the corner of four blocks. Extensive experiments and comparison with other deblocking methods have been conducted on the basis of PSNR, MSSIM, SF, and MOS to justify the effectiveness of the proposed method. The proposed algorithm keeps the computation lower and achieves better detail preservation and artifact removal performance.

Improved Method for the Macroblock-Level Deblocking Scheme

This paper presents a deblocking method for video compression in which the blocking artifacts are effectively extracted and eliminated based on both spatial and frequency domain operations. Firstly, we use a probabilistic approach to analyze the performance of the conventional macroblock-level deblocking scheme. Then, based on the results of the analysis, an algorithm to reduce the computational complexity is introduced. Experimental results show that the proposed algorithm outperforms the conventional video coding methods in terms of computation complexity while coding efficiency is maintained.

Macroblock-Level Deblocking Method to Improve Coding Efficiency for H.264/AVC

A macroblock-level deblocking method is proposed for H.264/AVC, in which blocking artifacts are effectively eliminated in the discrete cosine transform domain at the macroblock encoding stage. Experimental results show that the proposed algorithm outperforms conventional H.264 in terms of coding efficiency, and the bitrate saving is up to 5.7% without reconstruction quality loss.

Efficient Image Deblocking Based on Postfiltering in Shifted Windows

We propose a simple yet effective deblocking method for JPEG compressed image through postfiltering in shifted windows (PSW) of image blocks. The MSE is compared between the original image block and the image blocks in shifted windows, so as to decide whether these altered blocks are used in the smoothing procedure. Our research indicates that there exists strong correlation between the optimal mean squared error threshold and the image quality factor Q, which is selected in the encoding end and can be computed from the quantization table embedded in the JPEG file. Also we use the standard deviation of each original block to adjust the threshold locally so as to avoid the over-smoothing of image details. With various image and bit-rate conditions, the processed image exhibits both great visual effect improvement and significant peak signal-to-noise ratio gain with fairly low computational complexity. Extensive experiments and comparison with other deblocking methods are conducted to justify the effectiveness of the proposed PSW method in both objective and subjective measures.