Improved hybrid layered image compression using deep learning and traditional codecs

Abstract

Recently deep learning-based methods have been applied in image compression and achieved many promising results. In this paper, we propose an improved hybrid layered image compression framework by combining deep learning and the traditional image codecs. At the encoder, we first use a convolutional neural network (CNN) to obtain a compact representation of the input image, which is losslessly encoded by the FLIF codec as the base layer of the bit stream. A coarse reconstruction of the input is obtained by another CNN from the reconstructed compact representation. The residual between the input and the coarse reconstruction is then obtained and encoded by the H.265/HEVC-based BPG codec as the enhancement layer of the bit stream. Experimental results using the Kodak and Tecnick datasets show that the proposed scheme outperforms the state-of-the-art deep learning-based layered coding scheme and traditional codecs including BPG in both PSNR and MS-SSIM metrics across a wide range of bit rates, when the images are coded in the RGB444 domain.Keywords deep learning-based image coding · layered image coding · residual coding · convolutional neural network · autoencoderRecently with the rapid development of deep learning theory, especially after the successful applications of convolutional neural networks (CNN) in computer vision, deep learning has been applied to many areas, including image compression. Some deep learning-based methods have outperformed traditional image codecs such as JPEG, JPEG2000, and the H.265/HEVC-based BPG image codec [13,1,2,6,8,11,12,21], demonstrating its great potentials. In [17], Toderici et al. proposed a variable-rate image compression framework for thumbnail images by using the recurrent neural arXiv:1907.06566v1 [eess.IV]

Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training

“…Haisheng Fu et al [13], Proposed a hybrid model using convolutional neural network for achieving a compact representation of input image, which is encoded by FLIF codec to achieve the best representation of the image. Mu Li et al [14] proposed a research article for a lossy image compression model using learning convolutional networks.…”

An Extended Hybrid Image Compression Based on Soft-to-Hard Quantification

“…Recently, Akbari et al [2] and Fu et al [4] combine deep learning and traditional standard coding to form a hybrid image coding framework for improving the compression performance. The hybrid lossy image coding scheme only needs to train a basic model, which can encode traditional coding quantization factors to achieve different compression bits of the network.…”

“…The performance of DSSLIC outperforms the BPG codec in the RGB444 domain in both PSNR and MS-SSIM [6] metrics. Fu et al [4] simplified the hybrid compression framework by removing the semantic segmentation layer based on [2]. The convolutional neural networks were adopted to generate the coarse reconstructed image in [4] instead of the conditional GANs [16].…”

“…In this paper, we further develop an extended lossy hybrid image compression scheme based on [2], [4]. The scheme only has two layers as [4].…”

“…In this paper, we further develop an extended lossy hybrid image compression scheme based on [2], [4]. The scheme only has two layers as [4]. The compact representation C of the input image is encoded by the FLIF as the base layer.…”