Wavelet-Based Deep Auto Encoder-Decoder (WDAED)-Based Image Compression

In this work, we propose a Wavelet-based Deep Auto Encoder-Decoder Network (WDAED) based image compression which takes care of the various frequency components present in an image. Specifically, we demonstrate improvements over prior approaches utilizing this framework by introducing: (a) wavelet tr...

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Vydáno v:IEEE transactions on circuits and systems for video technology Ročník 31; číslo 4; s. 1452 - 1462
Hlavní autoři: Mishra, Dipti, Singh, Satish Kumar, Singh, Rajat Kumar
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
autoencoder
CNN
Coders
compression
Computing time
Convolutional codes
Datasets
Decoding
deep
DWT
Encoders-Decoders
frequency
Haar
Image coding
Image compression
Image quality
Image resolution
Machine learning
Wavelet
Wavelet transforms
ISSN:1051-8215, 1558-2205
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Popis
Shrnutí:In this work, we propose a Wavelet-based Deep Auto Encoder-Decoder Network (WDAED) based image compression which takes care of the various frequency components present in an image. Specifically, we demonstrate improvements over prior approaches utilizing this framework by introducing: (a) wavelet transform pre-processing for decomposing image into different frequencies for their separate processing (b) a very deep super-resolution network as a decoder of the convolutional autoencoder in order to achieve a good quality decompressed image. The end-to-end learning is performed for four wavelet sub-bands in parallel, minimizing the computational time. The encoder compresses the image by generating the latent space representations, whereas the decoder transforms the latent space to image space. The algorithm has been tested on various standard datasets i.e., ImageNet, Set 5, Set 14, Live 1, Kodak, Classic 5, General 100 and CLIC 2019 dataset. The proposed algorithm clearly exhibited the compression performance improvement of approximately 5%, 5.5%, and 13% in terms of PSNR, PSNRB and SSIM respectively.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2020.3010627