Image Compression is an application of data compression for digital images to lower their storage and/or transmission requirements.
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The application of machine learning(ML) and genetic programming(GP) to the image compression domain has produced promising results in many cases.
We present PLONQ, a progressive neural image compression scheme which pushes the boundary of variable bitrate compression by allowing quality scalable coding with a single bitstream.
At a high level, neural compression is based on an autoencoder that tries to reconstruct the input instance from a (quantized) latent representation, coupled with a prior that is used to losslessly compress these latents.
Recently deep learning-based image compression has shown the potential to outperform traditional codecs.
As the existing HDR quality datasets are limited in size, we created a Unified Photometric Image Quality dataset (UPIQ) with over 4, 000 images by realigning and merging existing HDR and standard-dynamic-range (SDR) datasets.
More specifically, we designed a compressor network with Refine-Net for coding residual signals and motion vectors.