Densely connected AutoEncoders for image compression
Densely connected AutoEncoders for image compression
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DOI：
10.1145/3313950.3313965
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Zebang Song;S. Kamata
中科院分区：
文献类型：
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作者：
Zebang Song;S. Kamata
图像压缩是一种应用于数字图像的数据压缩，几十年来一直是一个基础研究课题。最近的图像技术产生非常大量的数据，这可能使得在不使用压缩的情况下禁止图像数据的存储和通信。然而，传统的压缩方法，如JPEG，可能会引入压缩伪像问题。最近，深度学习在许多计算机视觉任务中取得了巨大成功，并逐渐被用于图像压缩。为了解决压缩失真问题，本文提出了一种有损图像压缩架构，该架构利用了现有深度学习方法的优点，以实现高编码效率。我们设计了一个密集连接的自动编码器结构的有损图像压缩。首先，我们设计了一个密集的自动编码器结构，以获得更丰富的图像特征信息，这将有助于压缩。其次，我们设计了一个类似U网的网络，以减少压缩造成的失真。最后采用改进的二值化器对编码器输出进行二值化处理。在低比特率的图像压缩中，实验表明，该方法的性能明显优于JPEG和JPEG2000，可以产生更好的视觉效果，具有清晰的边缘，丰富的纹理，和较少的伪影。
Image compression, which is a type of data compression applied to digital images, has been a fundamental research topic for many decades. Recent image techniques produce very large amounts of data, which may make it prohibitive to storage and communications of image data without the use of compression. However, the traditional compression methods, such as JPEG, may introduce the compression artefact problems. Recently, deep learning has achieved great success in many computer vision tasks and is gradually being used in image compression. To solve the compression atrefact problem, in this paper, we present a lossy image compression architecture, which utilizes the advantages of the existing deep learning methods to achieve a high coding efficiency. We design a densely connected autoencoder structure for lossy image compression. Firstly, we design a densely autoencoder structure to get richer feature information from image which can be helpful for compression. Secondly, we design a U-net like network to decrease the distortion caused by compression. Finally, an improved binarizer is adopted to quantize the output of encoder. In low bit rate image compression, experiments show that our method significantly outperforms JPEG and JPEG2000 and can produce a better visual result with sharp edges, rich textures, and fewer artifacts.