With the advent of learned image compression, numerous models have been developed. These models make use of non-lineartransforms that are learnt during the training process, where an image is transformed into a laten...
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ISBN:
(纸本)9781510679344;9781510679351
With the advent of learned image compression, numerous models have been developed. These models make use of non-lineartransforms that are learnt during the training process, where an image is transformed into a latent space, quantized and entropy coded. At the decoder, the quantized latent is recovered and transformed back to image space through a synthesis transform. In this work, we attempt to present an analysis of the energy distribution across channels. In our prior works, we demonstrated the features captured by the analysis transform, that can provide insights into the bitrate distribution across channels. Building on that, we extend our findings with quantitative measurements. We consider various learned image codecs that are based on the variational autoencoder framework and compare them with Karhunen Loeve transform (KLT) in terms of energy compaction. We also compare the closeness of the learned transforms to KLT to study the relationship between the design of classical codecs and learned codecs.
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