Abstract
Recently, the deep learning methods have been widely used in lossy compression schemes, greatly improving image compression performance. In this paper, we propose an extended hybrid image compression scheme based on soft-to-hard quantification, which has only two layers. The compact representation of the input image is encoded by the FLIF codec as the base layer. The residual of the input image and the reconstructed image is encoded by the BPG codec as the enhancement layer. The results using the Kodak and Tecnick datasets show that the performance of our proposed methods exceeds some image compression schemes based on deep learning methods and some traditional coding standards including BPG in SSIM metric across a wide range of bit rates, when the images are coded in the RGB444 domain. We explore the issue of bit rates allocation of the base layer and enhancement layer and the impact of enhancement layer codecs. Also, we analyze the limitations of the hybrid coding scheme.
Highlights
Deep neural networks (DNNs) have been applied in various areas and have made tremendous progress resulting from their superior optimization and representation learning performance
III, we evaluate the performance of the proposed scheme by comparing it with some compression framework based on deep learning methods including [2], [4] and some traditional codecs including BPG, JPEG, JPEG2000 and WebP codecs in the RGB444 domain
THE PROPOSED ENHANCED HYBRID IMAGE COMPRESSION FRAMEWORK we propose a hybrid lossy image coding framework based on the soft-to-hard quantification method
Summary
Deep neural networks (DNNs) have been applied in various areas and have made tremendous progress resulting from their superior optimization and representation learning performance. Most end-to-end lossy image compression schemes based on deep learning methods [1], [2], [13], [15] were proposed to handle these issues and achieved better compression performance and visual quality than JPEG2000 and the H.265/HEVC-based BPG image codec [18]. The learning-based image compression methods mainly consist of three operations: transformation, quantization, and entropy coding.
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