Learned image and video compression via autoencoders with spatial-temporal energy compaction penalties outperforms standards on MS-SSIM and visual quality.
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
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abstract
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result. First, we show that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to several metrics. Second, we modify the recurrent architecture to improve spatial diffusion, which allows the network to more effectively capture and propagate image information through the network's hidden state. Finally, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited number of bits to encode visually complex image regions. We evaluate our method on the Kodak and Tecnick image sets and compare against standard codecs as well recently published methods based on deep neural networks.
fields
eess.IV 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
A learned image compression system using deep residual learning and sub-pixel convolution reaches 0.972 MS-SSIM at 0.15 bits per pixel in the CLIC validation phase.
citing papers explorer
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Learning Image and Video Compression through Spatial-Temporal Energy Compaction
Learned image and video compression via autoencoders with spatial-temporal energy compaction penalties outperforms standards on MS-SSIM and visual quality.
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Deep Residual Learning for Image Compression
A learned image compression system using deep residual learning and sub-pixel convolution reaches 0.972 MS-SSIM at 0.15 bits per pixel in the CLIC validation phase.