Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high compression rate with visual artifacts degrading the user experience. Deep convolutional neural networks have become a widespread tool to address high-level computer vision tasks very successfully. Recently, they have found their way into the areas of low-level computer vision and image processing to solve regression problems mostly with relatively shallow networks. We present a novel 12-layer deep convolutional network for image compression artifact suppression with hierarchical skip connections and a multi-scale loss function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an improvement of up to 0.36 dB over the best previous ConvNet result. We show that a network trained for a specific quality factor (QF) is resilient to the QF used to compress the input image — a single network trained for QF 60 provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.
Traditional approaches to suppress compression artifacts can be split into three categories. Various types of intelligent edge-aware denoising such as SA-DCT @cite_29 @cite_3 , BM3D @cite_14 have been proposed to address this task during the late 2000s. In recent years, dictionary-based sparse recovery algorithms such as DicTV @cite_34 , RTF @cite_28 , S-D2 @cite_9 , @math @cite_6 , DDCN @cite_22 have achieved outstanding results by directly addressing the deficiencies such as ringing and blocking very specific to JPEG. These algorithms explicitly attempt to optimally reverse the effect of DCT-domain quantization using learned dictionaries very specific to the applied compressor and quantization tables.
@cite_14 @cite_22 @cite_28 @cite_29 @cite_9 @cite_3 @cite_6 @cite_34
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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. It is well known that at low-bit-rate block discrete cosine transform compressed image exhibits visually annoying blocking and ringing artifacts. In this paper, we propose a noniterative, wavelet-based deblocking algorithm to reduce both types of artifacts. The algorithm exploits the fact that block discontinuities are constrained by the dc quantization interval of the quantization table, as well as the behavior of wavelet modulus maxima evolution across wavelet scales to derive appropriate threshold maps at different wavelet scales. Since ringing artifacts occur near strong edges, which can be located either along block boundaries or within blocks, suppression of block discontinuities does not always reduce ringing artifacts. By exploiting the behavior of ringing artifacts in the wavelet domain, we propose a simple yet effective method for the suppression of such artifacts. The proposed algorithm can suppress both block discontinuities and ringing artifacts effectively while preserving true edges and textural information. Simulation results and extensive comparative study with both iterative and noniterative methods reported in the literature have shown the effectiveness of our algorithm. Blocking artifacts continue to be among the most serious defects that occur in images and video streams compressed to low bit rates using block discrete cosine transform (DCT)-based compression standards (e.g., JPEG, MPEG, and H.263). It is of interest to be able to numerically assess the degree of blocking artifact in a visual signal, for example, in order to objectively determine the efficacy of a compression method, or to discover the quality of video content being delivered by a web server. We propose new methods for efficiently assessing, and subsequently reducing, the severity of blocking artifacts in compressed image bitstreams. The method is blind, and operates only in the DCT domain. Hence, it can be applied to unknown visual signals, and it is efficient since the signal need not be compressed or decompressed. In the algorithm, blocking artifacts are modeled as 2-D step functions. A fast DCT-domain algorithm extracts all parameters needed to detect the presence of, and estimate the amplitude of blocking artifacts, by exploiting several properties of the human vision system. Using the estimate of blockiness, a novel DCT-domain method is then developed which adaptively reduces detected blocking artifacts. Our experimental results show that the proposed method of measuring blocking artifacts is effective and stable across a wide variety of images. Moreover, the proposed blocking-artifact reduction method exhibits satisfactory performance as compared to other post-processing techniques. The proposed technique has a low computational cost hence can be used for real-time image video quality monitoring and control, especially in applications where it is desired that the image video data be processed directly in the DCT-domain. We propose a highly accurate approach to remove artifacts of JPEG-compressed images. Our approach jointly learns a very deep convolutional network in both DCT and pixel domains. The dual-domain representation can make full use of DCT-domain prior knowledge of JPEG compression, which is usually lacking in traditional network-based approaches. At the same time, it can also benefit from the prowess and the efficiency of the deep feed-forward architecture, in comparison to capacity-limited sparse-coding-based approaches. Two simple strategies, i.e., Adam and residual learning, are adopted to train the very deep network and later proved to be a success. Extensive experiments demonstrate the large improvements of our approach over the state of the arts.