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Image compression is a method to remove spatial redundancy between adjacentpixels and reconstruct a high-quality image. In the past few years, deeplearning has gained huge attention from the research community and producedpromising image reconstruction results. Therefore, recent methods focused ondeveloping deeper and more complex networks, which significantly increasednetwork complexity. In this paper, two effective novel blocks are developed:analysis and synthesis block that employs the convolution layer and GeneralizedDivisive Normalization (GDN) in the variable-rate encoder and decoder side. Ournetwork utilizes a pixel RNN approach for quantization. Furthermore, to improvethe whole network, we encode a residual image using LSTM cells to reduceunnecessary information. Experimental results demonstrated that the proposedvariable-rate framework with novel blocks outperforms existing methods andstandard image codecs, such as George's ~\cite{002} and JPEG in terms of imagesimilarity. The project page along with code and models are available athttps://khawar512.github.io/cvpr/

Image Compression with Recurrent Neural Network and Generalized Divisive Normalization