Proceedings Article | 1 April 2020
KEYWORDS: Image compression, Image quality, Neural networks, Quantization, Computer programming, Visualization, Data modeling, RGB color model, Transform theory, Image classification
Nowadays, image and video are the data types that consume most of the resources of modern communication channels, both in fixed and wireless networks. Thus, it is vital to compress visual data as much as possible, while maintaining some target quality level, to enable efficient storage and transmission. Deep learning (DL) image coding solutions, typically using an auto-encoder architecture, promise significant improvements in compression efficiency. These methods adopt a novel coding approach where the encoder-decoder architecture is mostly based on neural networks, notably with analysis and synthesis transforms learned from a large amount of training data and an appropriate loss function. There are limited amount of works targeting the subjective evaluation of DL learning-based image coding solutions compression performance. Since learning-based image codecs use complex and highly non-linear generative models, very different artifacts are present in the decoded images, when compared to conventional artifacts such as blockiness, blurring and ringing distortions typical of traditional DCT block-based and wavelet image coding. In this context, the main objective of this paper is to review, characterize and evaluate some of the most relevant learning-based image coding solutions in the literature. Regarding the subjective quality evaluation, the assessment tests were conducted during the 84th JPEG meeting in Brussels, Belgium, by a mix of experts and naive observers. These subjective tests evaluated the performance of five state-of-the-art learning-based image coding solutions against four conventional, standard image coding (HEVC, WebP, JPEG 2000 and JPEG), applied to eight natural images, at four different coding bitrates. The experimental results obtained show that the subjective quality obtained with the selected learning-based image coding solution are competitive with conventional codecs. Moreover, a thorough inspection on the visual results has revealed some of the typical artifacts encountered in the learning -based image coding.
