Abstract
In this paper we propose a deep residual autoencoder exploiting Residual-in-Residual Dense Blocks (RRDB) to remove artifacts in JPEG compressed images that is independent from the Quality Factor (QF) used. The proposed approach leverages both the learning capacity of deep residual networks and prior knowledge of the JPEG compression pipeline. The proposed model operates in the YCbCr color space and performs JPEG artifact restoration in two phases using two different autoencoders: the first one restores the luma channel exploiting 2D convolutions; the second one, using the restored luma channel as a guide, restores the chroma channels explotining 3D convolutions. Extensive experimental results on three widely used benchmark datasets (i.e. LIVE1, BDS500, and CLASSIC-5) show that our model is able to outperform the state of the art with respect to all the evaluation metrics considered (i.e. PSNR, PSNR-B, and SSIM). This results is remarkable since the approaches in the state of the art use a different set of weights for each compression quality, while the proposed model uses the same weights for all of them, making it applicable to images in the wild where the QF used for compression is unkwnown. Furthermore, the proposed model shows a greater robustness than state-of-the-art methods when applied to compression qualities not seen during training.
Highlights
Image compression represents a very active research topic due to the high impact of the data in a large number of fields, from image sharing on the web to the most specific applications involving the acquisition of images and transfer to elaboration nodes
PROPOSED METHOD The methods in the state of the art mainly suffer from two limits: the first one is that each machine learning model needs to know the JPEG compression Quality Factor (QF) of each input image to properly restore a compressed image; the second one is that the great majority of them are capable to restore only the luma channel without considering the chroma components
The proposed model operates in the YCbCr color space and performs a two-phase restoration of JPEG artifacts: in the former phase, a first autoencoder exploiting 2D convolutions is used to restore the luma channel of the input image
Summary
Image compression represents a very active research topic due to the high impact of the data in a large number of fields, from image sharing on the web to the most specific applications involving the acquisition of images and transfer to elaboration nodes. Image compression refers to the task of representing images using the smallest storage space possible. Compression algorithms play a key role in saving space and bandwidth for the memorization and transfer of large amounts of images. The JPEG compression algorithm first converts the original RGB image into YCbCr color space and processes the luma and chroma channels separately. It divides the luma channel of an input image into non-overlapping 8 × 8
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