Proceedings ArticleDOI
Blocking artifact free inverse discrete cosine transform
Seungjoon Yang,Surin Kittitornkun,Yu Hen Hu,T.Q. Nguyen,D.L. Tull +4 more
- Vol. 3, pp 869-872
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TLDR
The generalized lapped biorthogonal transform embedded inverse discrete cosine transform (ge-IDCT) with nonlinear weighting in the embedded transform domain can reconstruct the signal with alleviated blockishness.Abstract:Â
This paper presents the generalized lapped biorthogonal transform embedded inverse discrete cosine transform (ge-IDCT) as an alternative to the IDCT. The ge-IDCT with nonlinear weighting in the embedded transform domain can reconstruct the signal with alleviated blockishness. Additional complexity, imposed by the replacement, is trivial thanks to an efficient lattice structure. The proposed ge-IDCT is applied in the JPEG still image compression standard to demonstrate its validity.read more
Citations
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Deep Generative Adversarial Compression Artifact Removal
TL;DR: In this article, a generative adversarial network (GAN) is proposed to produce more photorealistic details than MSE or SSIM-based networks, which can be used as a pre-processing step for object detection in case images are degraded by compression to a point that state-of-the art detectors fail.
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Deep Generative Adversarial Compression Artifact Removal
TL;DR: This work presents a feed-forward fully convolutional residual network model trained using a generative adversarial framework and shows that this model can be used as a pre-processing step for object detection in case images are degraded by compression to a point that state-of-the art detectors fail.
Compression Artifacts Removal Using Convolutional Neural Networks
TL;DR: This paper shows that it is possible to train large and deep convolutional neural networks for JPEG compression artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to previously used smaller networks as well as to any other state-of-the-art methods.
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Compression Artifacts Removal Using Convolutional Neural Networks
TL;DR: In this article, a CNN was used for JPEG compression artifacts reduction, which can provide significantly better reconstruction quality compared to previously used smaller networks as well as to any other state-of-the-art methods.
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Deep Universal Generative Adversarial Compression Artifact Removal
TL;DR: This work proposes an image transformation approach based on a feedforward fully convolutional residual network model and shows that this model can be optimized either traditionally, directly optimizing an image similarity loss (SSIM), or using a generative adversarial approach (GAN).
References
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Journal ArticleDOI
The LOT: transform coding without blocking effects
TL;DR: An exact derivation of an optimal lapped orthogonal transform (LOT) is presented, related to the discrete cosine transform (DCT) in such a way that a fast algorithm for a nearly optimal LOT is derived.
Journal ArticleDOI
An optimization approach for removing blocking effects in transform coding
S. Minami,Avideh Zakhor +1 more
TL;DR: The authors propose a new approach for reducing the blocking effect which can be applied to conventional transform coding without introducing additional information or significant blurring, and is based on the gradient projection method.
Journal ArticleDOI
The GenLOT: generalized linear-phase lapped orthogonal transform
TL;DR: A class of lapped orthogonal transforms with extended overlap (GenLOTs) is developed as a subclass of the general class of LPPUFB as a method to process finite-length signals.
Proceedings ArticleDOI
The generalized lapped biorthogonal transform
TL;DR: A lattice structure based on the singular value decomposition (SVD) is introduced that can be proven to use a minimal number of delay elements and to completely span a large class of M-channel linear phase perfect reconstruction filter banks (LPPRFB).