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# System prepended metadata

title: YUV Paper

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# YUV Paper

### 1. [Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color paces](https://arxiv.org/abs/2103.01760)
* IEEE Open Journal of Signal Processing

    **1.** based on input-output channel alignment
    * Which aim to support YUV420 without introducing any major changes to the existing network architectures.
    * Separate-channel Coding
        >Y, U and V components are split channel-wise, using single and  double encoder-input/decoder-output channel.
        >![](https://i.imgur.com/8ybEc7D.png =60%x)
    * Six-channel Coding
        >Luma component(Y) is spatially split into four channels by matching the dimensions of chroma components. This approach only requires training a single network.
        >![](https://i.imgur.com/WRGoKkZ.png)
    
    **2.** Proposed Transform Network Architecture
    *  Branched network structure
        > chroma branch does not apply any downsampling
    *  Cross-channel transformation
        >1×1 convolutional layer is used to combine the output of branched network carrying luma and chroma information separately at the encoder side. 
    *  Activation functions
        >Change activation into PReLU.


### 2. [Learned Variable-Rate Multi-Frequency Image Compression using Modulated Generalized Octave Convolution](https://arxiv.org/abs/2009.13074)
* MMSP 2020
*  In terms of YUV PSNR, the scheme is very similar to HEVC.
* Generalized from the recently developed octave convolution, where feature maps are factorized into high-frequency (HF) and low-frequency (LF) components, and the LF components have lower resolution than the HF components.
![](https://i.imgur.com/4NThcIM.png)
![](https://i.imgur.com/LhKhqev.png)
