Review: DS-CNN — Decoder-side Scalable CNN (Codec Filtering)

Scalable Quality Enhancement Approach, Outperform ARCNN and VRCNN

An Example of Application Scenario of DS-CNN

Outline

  1. From AR-CNN to AR-CNN-1 and AR-CNN-2
  2. DS-CNN-I and DS-CNN-B Network Architecture
  3. Experimental Results

1. From ARCNN to AR-CNN-1 and AR-CNN-2

AR-CNN
  • (Please feel free to read my ARCNN review if interested.)
  • AR-CNN-2: And adding one more layer. And PSNR is improved.

2. DS-CNN-I and DS-CNN-B Network Architecture

DS-CNN-I & DS-CNN-B Network Architecture
  • DS-CNN-B: It is used for inter-frame. Recall that Conv 1 is to extract intra coding features. Then, the outputs of Conv 1 and Conv 6 are concatenated, and are both convolved by Conv 7.
  • Thus, Conv 7 denoises the features of both intra and inter coding.
  • Conv 8–10 in DS-CNN-B are designed in the similar way.
Scalable Structure of DS-CNN
  • When the computational resources are sufficient, {S0 to S4} are turned on, and DS-CNN-B starts to work based on the output from the layers Conv 1–4 of DS-CNN-I.
  • Because of the reduction of inter coding distortion, the quality of B/P frames can be further enhanced by DS-CNN-B, at the cost of higher computational complexity.

3. Experimental Results

  • DS-CNN-B outperforms DS-CNN-I for B-frames as it has more feature maps for convolutions.
Performance Evaluation of DS-CNN

Reference

[2017 ICME] [DS-CNN]
Decoder-side HEVC Quality Enhancement with Scalable Convolutional Neural Network

My Previous Reviews

Image Classification [LeNet] [AlexNet] [Maxout] [NIN] [ZFNet] [VGGNet] [Highway] [SPPNet] [PReLU-Net] [STN] [DeepImage] [SqueezeNet] [GoogLeNet / Inception-v1] [BN-Inception / Inception-v2] [Inception-v3] [Inception-v4] [Xception] [MobileNetV1] [ResNet] [Pre-Activation ResNet] [RiR] [RoR] [Stochastic Depth] [WRN] [Shake-Shake] [FractalNet] [Trimps-Soushen] [PolyNet] [ResNeXt] [DenseNet] [PyramidNet] [DRN] [DPN] [Residual Attention Network] [DMRNet / DFN-MR] [IGCNet / IGCV1] [MSDNet] [ShuffleNet V1] [SENet] [NASNet] [MobileNetV2]

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