CAS-CNN: A deep convolutional neural network for image compression artifact suppression

By Lukas Cavigelli, Pascal Alexander Hager, Luca Benini. 2016

216 citations recorded by the source corpus.

Lossy image compression algorithms are pervasively used to reduce the size of images transmitted over the web and recorded on data storage media. However, we pay for their high compression rate with visual artifacts degrading the user experience. Deep convolutional neural networks have become a widespread tool to address high-level computer vision tasks very successfully. Recently, they have found their way into the areas of low-level computer vision and image processing to solve regression problems mostly with relatively shallow networks. We present a novel 12-layer deep convolutional network for image compression artifact suppression with hierarchical skip connections and a multi-scale loss function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an improvement of up to 0.36 dB over the best previous ConvNet result. We show that a network trained for a specific quality factor (QF) is resilient to the QF used to compress the input image — a single network trained for QF 60 provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Learned Video Compression (2018, background): “These include improved motion compensation and interpolation [54, 21, 58, 31], intra-prediction coding [40], post-processing refinement [7, 55, 26, 56, 48, 57, 41, 17], and rate control [28].”
Towards Flexible Blind JPEG Artifacts Removal (2021, methodology): “However, existing methods for JPEG artifacts removal generally have four limitations in real applications: (1) Most existing DNNs based methods [6, 7, 11, 28, 54] trained a specific model for each quality factor, lacking the flexibility to learn a single model for different JPEG quality factors.”
MambaIR: A Simple Baseline for Image Restoration with State-Space Model (2024, background): “Early attempts usually elaborate CNNs with techniques such as residual connection [6, 34], dense connection [68, 89] and others [13, 19, 36, 70] to improve model representation ability.”
Image and Video Compression With Neural Networks: A Review (2019, methodology): “More works utilized more complicated structure to improve the compressed images [112], [113].”
SwinIR: Image Restoration Using Swin Transformer (2021, unknown): “Since pioneering work SRCNN [18] (for image SR), DnCNN [90] (for image denoising) and ARCNN [17] (for JPEG compression artifact reduction), a flurry of CNN-based models have been proposed to improve model representation ability by using larger and deeper neural network architecture designs, such as ”
DiffIR: Efficient Diffusion Model for Image Restoration (2023, background): “Specifically, from level-1 to level-4, we set the number of dynamic transformer blocks to [1, 1, 1, 9], [13, 1, 1, 1], and [3, 5, 6, 6] for inpainting, SR, and deblurring, respectively.”
Multi-frame Quality Enhancement for Compressed Video (2018, background): “Recently, extensive works [25, 10, 33, 18, 19, 6, 9, 12, 35, 24, 4] have focused on enhancing the visual quality of compressed image.”