Design a deblocking filter with three separate modes in DCT-based coding

The reconstructed images from highly compressed data have noticeable image degradations, such as blocking artifacts near the block boundaries. Post-processing appears to be the most feasible solution because it does not require any existing standards to be changed. Markedly reducing blocking effects can increase compression ratios for a particular image quality or improve the quality of equally compressed images. In this work, a novel deblocking algorithm is proposed based on three filtering modes in terms of the activity across block boundaries. By properly considering the masking effect of the HVS (Human Visual System), an adaptive filtering decision is integrated into the deblocking process. According to three different deblocking modes appropriate for local regions with different characteristics, the perceptual and objective quality are improved without excessive smoothing the image details or insufficiently reducing the strong blocking effect on a flat region. According to the simulation results, the proposed method outperforms other deblocking algorithms in respect to PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural SIMilarity).     

Edge‐Preserving Algorithm for Block Artifact Reduction and Its Pipelined Architecture

This paper presents a new edge‐protection algorithm and its very large scale integration (VLSI) architecture for block artifact reduction. Unlike previous approaches using block classification, our algorithm utilizes pixel classification to categorize each pixel into one of two classes, namely smooth region and edge region, which are described by the edge‐protection maps. Based on these maps, a two‐step adaptive filter which includes offset filtering and edge‐preserving filtering is used to remove block artifacts. A pipelined VLSI architecture of the proposed deblocking algorithm for HD video processing is also presented in this paper. A memory‐reduced architecture for a block buffer is used to optimize memory usage. The architecture of the proposed deblocking filter is verified on FPGA Cyclone II and implemented using the ANAM 0.25 µm CMOS cell library. Our experimental results show that our proposed algorithm effectively reduces block artifacts while preserving the details. The PSNR performance of our algorithm using pixel classification is better than that of previous algorithms using block classification.     

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.     

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.     

低码率视频的小波域软阈值去方块滤波算法的研究

本文针对低码率条件下基于子块DCT的视频压缩产生的方块效应,提出了一种小波域上进行软阈值滤波的改进算法。新算法根据图像的平坦区和单调变化区等非纹理区易于通过平滑滤波去噪的性质,以非纹理区作为参考对象计算阈值,并且采用虚拟滤波的思想简化了阈值的计算,同时根据低码率压缩视频的特点修改了在低层子带的软阈值处理过程。对测试序列Susan的实验结果表明,新的算法不仅获得很好的主观去方块效果,而且可以对帧内和帧间编码模式平均分别获得约1.0dB和0.28dB的客观质量(PSNR)的改善,因此新的算法适用于可视电话等低码率视频的应用。     

A Fast-Deblocking Boundary-strength Based Architecture Design of Deblocking Filter in H.264/AVC Applications

This work presents an efficient architecture design for deblocking filter in H.264/AVC using a novel fast-deblocking boundary-strength (FDBS) technique. Based on the FDBS technique, the proposed architecture divides the deblocking process into three filtering modes, namely offset-based, standard-based and diagonal-based filtering modes, to reduce the blocking artifact and improve the video quality in H.264/AVC. The proposed architecture is designed in Verilog HDL, simulated with Quartus II and synthesized using 0.18 μm CMOS cells library with the Synopsys Design Compiler. Simulation results demonstrate good performance in PSNR improvement and bit-rate reduction. Additionally, verification results through physical chip design reveal that the proposed architecture design can support 1,280 × 720@30 Hz processing throughput while clocking at 100 MHz. Comparisons with other studies show the excellent properties of the proposed architecture in terms of gate count, memory size and clock-cycle/macroblock.

A neutrosophic filter for high-density Salt and Pepper noise based on pixel-wise adaptive smoothing parameter

Image indeterminacy has been neglected in most traditional filtering algorithms. This paper proposes a pixel-wise adaptive neutrosophic filter based on neutrosophic indeterminacy feature to remove high-level Salt-and-Pepper noise. In the proposed algorithm, the indeterminacy of a pixel is quantified by a Neutrosophic Set and innovatively exploited as an efficient characteristic of measuring the similarity of pixels. In order to adjust the smoothing parameter of the weight function pixel-wise adaptively, the uncertainty of a pixel is utilized as an indicator of image contents. Extensive experiments on numerous images demonstrate that with a 3 × 3 window, our method outperforms many existing denoising methods in terms of noise suppression and detail preservation.     

Enhanced wide-activated residual network for efficient and accurate image deblocking

To save bandwidth and storage space as well as speed up data transmission, people usually perform lossy compression on images. Although the JPEG standard is a simple and effective compression method, it usually introduces various visually unpleasing artifacts, especially the notorious blocking artifacts. In recent years, deep convolutional neural networks (CNNs) have seen remarkable development in compression artifacts reduction. Despite the excellent performance, most deep CNNs suffer from heavy computation due to very deep and wide architectures. In this paper, we propose an enhanced wide-activated residual network (EWARN) for efficient and accurate image deblocking. Specifically, we propose an enhanced wide-activated residual block (EWARB) as basic construction module. Our EWARB gives rise to larger activation width, better use of interdependencies among channels, and more informative and discriminative non-linearity activation features without more parameters than residual block (RB) and wide-activated residual block (WARB). Furthermore, we introduce an overlapping patches extraction and combination (OPEC) strategy into our network in a full convolution way, leading to large receptive field, enforced compatibility among adjacent blocks, and efficient deblocking. Extensive experiments demonstrate that our EWARN outperforms several state-of-the-art methods quantitatively and qualitatively with relatively small model size and less running time, achieving a good trade-off between performance and complexity.     

Low-complexity high-quality adaptive deblocking filter for H.264/AVC system

Blocking artifacts always appear on the reconstructed image, particularly in a low bit-rate video coding system. This paper presents an adaptive offset method to improve image quality for H.264 decoding. First, the histogram statistic is used to analyze the correlation between the offset and the filtering performance. The best filtering performance can mostly be found at the position of three offsets. Second, the best offset can be searched for with the minimum SAE (Summation of Absolute Error) among the three candidates. This algorithm can not only keep low computations, but it can also obtain good filtering quality. The average performance can be improved about 0.25 to 0.45 dB (decibels) higher than the original H.264 deblocking filter. The blocky effect on the decoding image can be smoothed in vision.     

An automatic region-based video sequence codec based on fractal compression

In order to improve the performance of fractal video coding, we explore a novel fractal video sequences codec with automatic region-based functionality. To increase the quality of decoding image, intra frame coding, deblocking loop filter and sub-pixel block matching are applied to the codec. An efficient searching algorithm is used to increase the compression ratio and encoding speed. Automatic region-based fractal video sequences coding reduces coding stream greatly. Experimental results indicate that the proposed algorithm is more robust, and provides much less encoding time and bitrate while maintaining the quality of decompression image than the conventional CPM/NCIM method and other related references. We compare the proposed algorithm with three algorithms in Refs. [24], [25], [26], and the results of all these four algorithms are compared with H.264. The bitrate of the proposed algorithm is decreased by 0.11% and the other algorithms are increased by 4.29%, 6.85% and 11.62%, respectively. The average PSNR degradations of the four algorithms are 0.71 dB, 0.48 dB, 0.48 dB and 0.75 dB. So the bitrate of the proposed algorithm is decreased and the other algorithms are increased. At the meantime the compression time is reduced greatly, about 79.19% on average. The results indicate that, on average, the proposed automatic region-based fractal video sequences coding system can save compression time 48.97% and bitrate 52.02% with some image quality degradation in comparison with H.264, since they are all above 32 dB and the human eyes are insensitive to the differences.     

Bilateral Filters with Adaptive Generalized Kernels Generated via Riemann-Lebesgue Theorem

This paper introduces families of bilateral filters for image denoising and sharpness enhancements, JPEG deblocking, and texture filtering. While the Gaussian distribution dictates the application of the bilateral filters, we introduce a wide variety of kernels based on Riemann-Lebesgue’s theorem. The derivation of the bilateral filters are established in both adaptive and non-adaptive approaches. The adaptation of the filters is adjusted via computing the variances (inflection points) using different methods based on applications. For image denoising and sharpness, the variance estimated using Laplacian-of-Gaussian filter followed by affine mapping. The variance is computed as a proportion of intensity differences across the boundary in JPEG deblocking. In texture filtering, the variance is calculated form modified relative variations. We carry out extensive experiments in three different applications and compare the results using different bilateral filters. The proposed filters are giving better results, compared with standard bilateral and adaptive bilateral filters.

     

Fragile watermarking scheme for tamper localization in images using logistic map and singular value decomposition

This paper presents a fragile watermarking scheme for tamper localization using Singular Value Decomposition (SVD) and logistic map. The proposed scheme divides the image into blocks of size 2 × 2 pixels and generates an 8-bit watermark from each block. The watermark is computed by permuting the six Most Significant Bits (MSBs) of each pixel in the block using the logistic map, followed by SVD. To secure, the watermark thus generated is further encrypted using the logistic map. This encrypted watermark is embedded into 2 Least Significant Bits (LSBs) of each pixel to enable tamper detection and localization. The experimental results demonstrate that the proposed scheme can precisely locate tampered regions under copy-paste, content removal, text addition, noise addition, vector quantization, collage, content only, and constant feature attacks. Tamper localization accuracy is better or comparable to the state-of-the-art tamper localization algorithms.     

Guest Editors’ Introduction

In H.264/AVC, a deblocking filter improves visual quality by reducing the presence of blocking artifacts in decoded video frames. The deblocking filter accounts for one third of the computational complexity of the decoder. This paper exploits the scalability on the hardware and the algorithmic level to synergize the performance and to reduce the computational complexity. First, we propose a modular deblocking filter architecture which can be scaled to adapt to the required computing capability for various bit-rates, resolutions, and frame rate of video sequences. The scalable architecture is based on FPGA using dynamic partial reconfiguration. This desirable feature of FPGAs makes it possible for different hardware configurations to be implemented during run-time. The proposed design can be scaled to filter up to four different edges simultaneously, resulting in significant reduction of total processing time. Secondly, our experiments show that significant reduction in computational complexity can be achieved by the increased presence of skipped macroblocks at lower bit-rates, thus, avoiding redundant filtering operations. The implemented architecture is evaluated using the Xilinx Virtex-4 ML410 FPGA board. The design operates at a maximum frequency of 103 MHz. The reconfiguration is done through Internal Configuration Access Port (ICAP) to achieve maximum performance needed by real time applications.     

Deblocking of block-transform compressed images using weighted sums of symmetrically aligned pixels.

A new class of related algorithms for deblocking block-transform compressed images and video sequences is proposed in this paper. The algorithms apply weighted sums on pixel quartets, which are symmetrically aligned with respect to block boundaries. The basic weights, which are aimed at very low bit-rate images, are obtained from a two-dimensional function which obeys predefined constraints. Using these weights on images compressed at higher bit rates produces a deblocked image which contains blurred "false" edges near real edges. We refer to this phenomenon as the ghosting effect. In order to prevent its occurrences, the weights of pixels, which belong to nonmonotone areas, are modified by dividing each pixel's weight by a predefined factor called a grade. This scheme is referred to as weight adaptation by grading (WABG). Better deblocking of monotone areas is achieved by applying three iterations of the WABG scheme on such areas followed by a fourth iteration which is applied on the rest of the image. We refer to this scheme as deblocking frames of variable size (DFOVS). DFOVS automatically adapts itself to the activity of each block. This new class of algorithms produces very good subjective results and PSNR results which are competitive relative to available state-of-the-art methods.     

Scalability of 2-D wavelet transform algorithms: analytical and experimental results on MPPs

This paper studies the scalability of two-dimensional (2-D) discrete wavelet transform (DWT) algorithms on massively parallel processors (MPPs). The principal operation in the 2-D DWT is the filtering operation used to implement the filter banks of the 2-D subband decomposition. This filtering operation can be implemented as a convolution in the time domain or as a multiplication in the frequency domain. We demonstrate that there exist combinations of machine size, image size, and wavelet kernel size for which the time-domain algorithms outperform the frequency domain algorithms and vice-versa. We therefore demonstrate that a hybrid approach that combines time- and frequency-domain approaches can yield linear scalability for a broad range of problem and machine sizes. Furthermore, we show the effect of processor speed versus communication overhead and the use of separable versus nonseparable wavelets on the crossover points between the algorithm approaches.     

基于矩阵范数分析的自适应数字水印算法

针对数字产品的版权保护问题,提出了一种基于矩阵范数分析的自适应数字水印算法.算法将载体图像分为8×8不重复块并进行DCT变换,根据8×8模板对每个分块的低、高频数据分别组成新矩阵并进行SVD分解.结合人眼视觉特性和矩阵范数理论推导结果求解对应的块能量确定嵌入位置.水印信息选择在摸板的低频区矩阵的奇异值分解的首个值中,嵌入强度随块能量比自动调节.理论分析和实验结果表明,该算法不仅具有较好的透明性,而且对常见攻击如高斯噪声、JPEG压缩、滤波以及几何攻击等具有较好的稳健性.     

Impulsive noise filtering based on noise detection in corrupted digital color images

This paper is an enhancement to our earlier research with grey-scale images. In this paper, we propose two new detection-estimation based image filtering algorithms that effectively remove corrupted pixels with impulsive noise in digital color images. The existing methods for enhancing corrupted color images typically possess inherent problems in computation time and smoothing out edges because all pixels are filtered. Our proposed algorithms first classify corrupted pixels in each channel or in each pixel. Because marginal or vector median filtering is only performed for the classified pixels, the process is computationally efficient, and edges are preserved well. In addition, because there is no appropriate criterion to evaluate the performance of impulsive noise detectors for color images, the objective comparison of noise detectors is difficult. Thus, we introduce a new efficiency factor for comparing the performance of noise detectors in digital color images. Simulation results show that the proposed algorithms perform better than existing methods, in both objective and subjective evaluations.This work was supported by the Korea Science & Engineering Foundation (KOSEF) under grant no. 981-0912-057-2.     

A new iterated two-band diffusion equation:theory and its application

We propose an iterated two-band filtering method to solve the selective image smoothing problem. We prove that a discrete computation step in an iterated nonlinear diffusion-based filtering algorithm is equivalent to a sequence of operations, including decomposition, regularization, and then reconstruction, in the proposed two-band filtering scheme. To correctly separate the high frequency components from the low frequency ones in the decomposition process, we adopt a dyadic wavelet-based approximation scheme. In the regularization process, we use a diffusivity function as a guide to retain useful data and suppress noises. Finally, the signal of the next stage, which is a "smoother" version of the signal at the previous stage, can be computed by reconstructing the decomposed low frequency component and the regularized high frequency component. Based on the proposed scheme, the smoothing operation can be applied to the correct targets. Experimental results show that our new approach is really efficient in noise removing.     

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.     

Perceptually adaptive joint deringing–deblocking filtering for scalable video transmission over wireless networks

Video transmission over low bit-rate channels, such as wireless networks, requires dedicated filtering during decoding for crucial enhancement of the perceptual video quality. For that reason, deringing and deblocking are inevitable components of decoders in wireless video transmission systems. Aimed at improving the visual quality of decoded video, in this paper a new perceptually adaptive joint deringing–deblocking filtering technique for scalable video streams is introduced. The proposed approach is designed to deal with artefacts inherent to transmissions over very low bit-rate channels, specifically wireless networks. It considers both prediction and update steps in motion compensated temporal filtering in an in-loop filtering architecture. The proposed approach integrates three different filtering modules to deal with low-pass, high-pass and after-update frames, respectively. The filter strength is adaptively tuned according to the number of discarded bit-planes, which in turn depends on the channel bit-rate and the channel error conditions. Furthermore, since ringing and blocking artefacts are visually annoying, relevant characteristics of the human visual system are considered in the used bilateral filtering model. That is, the amount of filtering is adjusted to the perceptual distortion by integrating a human visual system model into filtering based on luminance, activity and temporal masking. As a consequence, the resulting filter strength is automatically adapted to both perceptual sensitivity and channel variation. To assess the performance of the proposed approach, a comprehensive comparative evaluation against the conventional loop architecture and bilateral filter was conducted. The results of the experimental evaluation show a superior performance of the proposed adaptive filtering approach, providing better objective and subjective quality.