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Deblocking Scheme for JPEG-Coded Images Using Sparse Representation and All Phase Biorthogonal Transform

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Deblocking Scheme for JPEG-Coded Images Using Sparse Representation and All Phase Biorthogonal Transform Journal of Communications Vol. 11, No. 12, December 2016 Deblocking Scheme for JPEG-Coded Images Using Sparse Representation and All Phase Biorthogonal Transform Liping Wang, Chengyou Wang, and Xiao Zhou School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China Email: [email protected]; {wangchengyou, zhouxiao}@sdu.edu.cn Abstract—For compressed images, a major drawback is that The image deblocking algorithm aims to alleviate those images will exhibit severe blocking artifacts at very low blocking artifacts and improve visual quality of bit rates due to adopting Block-Based Discrete Cosine compressed images. Deblocking methods can be mainly Transform (BDCT). In this paper, a novel deblocking scheme divided into two categories: in-loop processing methods using sparse representation is proposed. A new transform called All Phase Biorthogonal Transform (APBT) was proposed in and post-processing methods. The in-loop deblocking recent years. APBT has the similar energy compaction property processing methods not only avoid the propagation of with Discrete Cosine Transform (DCT). It has very good blocking artifacts between adjacent frames, but also can column properties, high frequency attenuation characteristics, enhance coding efficiency. For example, H.264/AVC [2] low frequency energy aggregation, and so on. In this paper, we adopts the in-loop deblocking processing. Some use it to generate the over-completed dictionary for sparse researchers have designed the in-loop deblocking filter coding. For Orthogonal Matching Pursuit (OMP), we select an [4]. Numerous experimental results manifest that the in- adaptive residual threshold by combining blind image blocking assessment. Experimental results show that this new scheme is loop deblocking filter can provide both objective and effective in image deblocking and can avoid over-blurring of subjective improvement compared with the compressed edges and textures. We can obtain deblocked images in the images. The image deblocking methods which is receiver. performed after decompressed is called post-processing. Index Terms—Image deblocking, sparse representation, All Therefore, post-processing is more practical and used for Phase Biorthogonal Transform (APBT), Orthogonal Matching image deblocking. Pursuit (OMP) For removing and eliminating the blocking artifacts, numerous image deblocking approaches based on post- processing have been proposed, such as filtering methods I. INTRODUCTION [5] and Projection Onto Convex Sets (POCS) [6]. With the development and progress of technology, the Recently, learning-based sparse representation has been resolution of the imaging device is higher and higher, successfully used for image deblocking. Jung et al. [7] which leads to the amount of image data increasing obtained an over-completed dictionary using the K- exponentially. To solve the contradiction between the Singular Value Decomposition (K-SVD) algorithm [8], data rate and channel bandwidth, the massive data must and proposed new method to automatically estimate the be compressed to meet the needs of image transmission residual threshold for Orthogonal Matching Pursuit and storage. The Block-Based Discrete Cosine Transform (OMP) [9]. Instead of processing each image patch (BDCT) has good features of regularity and simplicity for individually, Zhang et al. [10] proposed Group-based hardware implementation. Therefore, BDCT is widely Sparse Representation (GSR), and a novel image utilized by several image and video international coding deblocking method is proposed based on GSR and standards, such as JPEG [1], H.264/AVC [2], and Quantization Constraint (QC) prior. Similarly, Zhao et al. H.265/HEVC [3]. However, at lower bit rates, the [11] proposed Structural Sparse Representation (SSR). blocking artifacts are caused by quantization error. The And combined with QC prior, a novel algorithm for blocking artifacts make image’s visual quality image deblocking was proposed. All of them degenerated. Besides, it also has a negative impact on the demonstrated that sparse representation can effectively further image processing. Therefore, image deblocking is remove image deblocking and ensure the image’s visual necessary and important, especially for highly quality. Our work has been inspired by the image compressed images. deblocking results based on sparse representation. In this paper, we will mainly focuses on image deblocking method for JPEG-coded images. Manuscript received August 28, 2016; revised December 20, 2016. For image deblocking method based on sparse This work was supported by the National Natural Science representation, a proper dictionary and sparse coding Foundation of China (No. 61201371), the promotive research fund for excellent young and middle-aged scientists of Shandong Province, algorithm are needed. Because All Phase Biorthogonal China (No. BS2013DX022), and the Natural Science Foundation of Transform (APBT) [12] has very good column properties, Shandong Province, China (No. ZR2015PF004). high frequency attenuation characteristics, low frequency Corresponding author email: [email protected]. energy aggregation, and so on. The dictionary based on doi:10.12720/jcm.11.12.1095-1101 ©2016 Journal of Communications 1095 Journal of Communications Vol. 11, No. 12, December 2016 APBT can reveal the sparsity of image better than the In our work, we use OMP due to its simplicity and dictionary based on the commonly used orthogonal basis. efficiency. In this paper, we use the APBT to produce the over- B. JPEG Model completed dictionary. For sparse coding algorithm, an adaptive residual threshold for OMP is used by The frame of the image compression standard used combining blind image blocking assessment. here is JPEG [1], which contains three basic steps: DCT, Experimental results show that our proposed scheme is DCT coefficient quantization, and Huffman entropy better than other methods. encoding. The decoding process is inverse process of The rest of this paper is organized as follows. Section encoding. The process of encoding is shown as follows: II concisely introduces the sparse representation and Step 1. The input image is first converted into the JPEG model. Section III presents the image deblocking YCrCb color space and then grouped into blocks of size scheme for JPEG compressed images using sparse 8×8. representation and APBT. In Section VI, experimental Step 2. Before carrying out a BDCT, the input image results and comparisons are presented. Finally, data are shifted from unsigned integers to signed integers. conclusion and further research are given in Section V. Step 3. Each block is transformed by BDCT. Each block will include 64 DCT coefficients composed of one II. SPARSE REPRESENTATION AND JPEG MODEL DC coefficient and 63 AC coefficients. Step 4. Quantize each matrix block. A. Sparse Representation Model Step 5. After quantization, the DC coefficient of each For image patches of size nn , they are converted block is conducted a differential pulse code modulation into column vectors x n in lexicographical order. In (DPCM) coding. Before entropy encoding, the 63 coefficients are scanned into a 1-D zig-zag sequence. order to construct sparse-land model, a dictionary of size D nk (with kn , implying that it is redundant) is III. PROPOSED SCHEME defined. In general, the dictionary is assumed to be known and fixed. According to the sparse representation The framework of our proposed method is shown in model, every image patch x could be represented Fig. 1. Our proposed method includes two parts: the sparsely over this dictionary D , generation of over-completed dictionary and sparse coding with adaptive residual thresholds. ˆ argmin || ||0 subject to Dx (1) Start Input image The || ||0 represents the number of the nonzero entries in . In general, ||ˆ || n . The basic principle Extracting 8×8 0 image blocks of sparse representation is that every signal can be Changing into considered as a linear combination of few columns of the column vectors dictionary D . Blocks matrix If we substitute a specific requirement of an allowable bounded representation error ||Dx ||2 for the Selecting 10000 columns rough constraint Dx , this sparse representation model will become more precise. For the sparse-land SubB matrix input signal x and the noisy output signal y which is SubB[i][j]=SubB[i][j]- means(SubB[:,j]) contaminated by white Gaussian noise with standard Selecting optimal APBT dictionary Coefs=OMP(SubB,D) deviation , the maximum a posteriori (MAP) estimator error residual T D for denoising this noisy signal is represented as SubB[:,j]=SubB[:,j]* Coefs+means(SubB[:,j]) 2 ˆ argmin || ||02 subject to ||Dy || T (2) N The blocks matrix finished? where T is residual threshold, and it is decided by and Y Image blocks . The optimization task of (2) can be also changed into restructuring 2 Deblocking image ˆ argmin ||Dy ||20 || || (3) End For a proper value of , the (2) and (3) are equivalent. Fig. 1. Flow chart of our proposed method At last, the denoised image can be obtained by xDˆ αˆ . Generally, (2) or (3) is very hard to solve. However, A. The Over-Completed APBT Dictionary we can efficiently solve this problem using several According to all phase digital filtering, there are three available approximation techniques, such as OMP [9], kinds of APBT based on WHT, DCT, and IDCT [12]. In Basis Pursuit (BP) [13], and Matching Pursuit (MP) [14]. this paper, we will focus on the APBT based on DCT and ©2016 Journal of Communications 1096 Journal of Communications Vol. 11, No. 12, December 2016 IDCT in order to compare with the DCT. Similar to DCT Similarly, APIDCBT (APBT based on IDCT) can be matrix, APBT can also be used to generate the over- denoted by completed dictionary for image spares representation. 1 Taking APDCBT (APBT based on DCT) for example, , m0,1, , N 1, n 0, N the process of two-dimensional APBT is shown as V (,)mn (6) N m 2 1 m (2 n 1)π mN0,1, , 1, follows. For a signal sequence with N points x and the cos , NN2 2 nN1,2, , 1. APDCBT matrix V with size of NN , the transform coefficients y can be denoted by (4) and (5) after two- Fig.
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