基于深度卷积神经网络的信息流增强图像压缩方法

doi:10.13229/j.cnki.jdxbgxb20190793

Abstract

Abstract:

In the image compression process, the compression efficiency is largely affected by the information utilization. In order to improve the utilization of information, in this paper, we propose an end-to-end image compression method with information enhancement based on deep convolutional networks. In the encoding and decoding networks, a special network structure is adopted, which can both increase the forward and backward connections between the convolutional layers. As a result, the bidirectional information flow and the cyclic feedback of visual features are realized, which can effectively reduce the number of bits required for image compression. Experimental results show that, at the same compression ratio, the multiscale structural similarity index (MS-SSIM) of the reconstructed image of the proposed method can gain 0.08, 0.027 and 0.012 higher than JPEG, JPEG2000 and HEVC, respectively. The information enhancement structure based on deep convolutional network can effectively improve the coding efficiency when it is used for image compression.

Key words: image compression, auto encoder, convolution networks, information enhancement

CLC Number:

TN919.81

Zhi-jun LI,Chu-xi YANG,Dan LIU,Da-yang SUN. Deep convolutional networks based image compression with enhancement of information flow[J].Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1788-1795.

Figures/Tables 6

Fig.1

Table 1

Process of feature extraction of information enhancement block"

总输入	第一阶段			第二阶段			总输出
总输入	输入特征	权重系数	输出	输入特征	权重系数	输出	总输出
$X 0$	$X 0$	$w 01$	$X 1 (1)$	$X 2 (1), X 3 (1), X 4 (1)$	$w 21, w 31, w 41$	$X 1 (2)$	$X 0, X 1 (2), X 2 (2), X 3 (2), X 4 (2)$
	$X 0, X 1 (1)$	$w 02, w 12$	$X 2 (1)$	$X 3 (1), X 4 (1), X 1 (2)$	$w 32, w 42, w 12$	$X 2 (2)$
	$X 0, X 1 (1), X 2 (1)$	$w 03, w 13, w 23$	$X 3 (1)$	$X 4 (1), X 1 (2), X 2 (2)$	$w 43, w 13, w 23$	$X 3 (2)$
	$X 0, X 1 (1), X 2 (1), X 3 (1)$	$w 04, w 14, w 24, w 34$	$X 4 (1)$	$X 1 (2), X 2 (2), X 3 (2)$	$w 14, w 24, w 34$	$X 4 (2)$

Table 1

Fig.2

Fig.3

Fig.4

Fig.5

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Deep convolutional networks based image compression with enhancement of information flow

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