Image Compression Using LSTM/GRU Based Recurrent Convolution Neural Network

• Main Authors: Ting-Xuan Wu, 吳亭萱
• Other Authors: Jiann-Jone Chen
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/bqk75g

碩士 === 國立臺灣科技大學 === 電機工程系 === 106 === Performances of Image/video encoding methods, such as robustness to error attack and compression ratio, are important for multimedia communication applications. The JPEG image compression standard adopts block-based discrete cosine transform (DCT) and the JPEG2000 utilize wavelet transform (WT) to provide multi-resolution compression. Both standards are efficient for current multimedia communication standards. In this research, we study how to utilize deep learning methods to compress image signals, which can provide comparable performances with DCT-based JPEG and WT-based JPEG2000. We proposed an auto encoder architecture for image compression based on a multi-layer recurrent convolutional neural network that comprises an encoder and a decoder sub models. For network nodes, we use long short-term memory network (LSTM) or GRU (Gated Recurrent Units) to enable efficient information delivery. In the training phase, both encoding and decoding procedures are trained together to reserve the most image feature information during compression and decompression. . In the testing phase, it executes encoding and decoding procedures separately to verify performances. Experiments showed that when BPP > 0.8, the PSNR and MS-SSIM (Multiscale Structure Similarity) performances of the proposed methods are better than those of JPEG. For high compression BPP < 0.8, the proposed method outperforms JPEG and others in MS-SSIM, which demonstrates better visual perception performance, i.e., sharper edge, rich textures and fewer artifacts.