Review: DCAD — Deep CNN-based Auto Decoder (Codec Post-Processing)
Achieve 5.0%, 6.4%, 5.3%, 5.5% BD-Rate Reduction for All Intra, Low Delay P, Low Delay B, and Random Access Configurations Respectively Compared to HEVC
In this story, Deep CNN-based Auto Decoder (DCAD), by Sun Yat-sen University, is briefly reviewed. For some applications limited by the bandwidth and storage, e.g. surveillance, the high compression ratio is usually used, which will heavily affect the accuracy of the follow-up computer vision tasks, such as retrieval, detection, and recognition, using the decoded videos as inputs. By using DCAD, artifacts are removed and details are enhanced for the HEVC-compressed videos after decoding. This is the paper in 2017 DCC with more than 30 citations. (Sik-Ho Tsang @ Medium)
- DCAD Network Architecture
- Experimental Results
1. DCAD Network Architecture
- A stack of 3×3 convolutional filters are used, just like VGGNet.
- Only ReLU is used without max pooling.
- The depth is 10. Depth of 20 is also tried but without significant coding gains.
- Mean square error (MSE) is used as loss function.
2. Experimental Results
- DCAD achieves 5.0%, 6.4%, 5.3%, 5.5% BD-Rate reduction for All Intra (AI), Low Delay P (LDP), Low Delay B (LDB), and Random Access (RA) configurations respectively compared to HEVC.
- Using GeForce GTX 980T, for a 1080p frame, with the use of CUDNN, 0.0090 sec is needed.
- And without CUDNN, 0.653 sec is needed.
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]
Object Detection [OverFeat] [R-CNN] [Fast R-CNN] [Faster R-CNN] [MR-CNN & S-CNN] [DeepID-Net] [CRAFT] [R-FCN] [ION] [MultiPathNet] [NoC] [Hikvision] [GBD-Net / GBD-v1 & GBD-v2] [G-RMI] [TDM] [SSD] [DSSD] [YOLOv1] [YOLOv2 / YOLO9000] [YOLOv3] [FPN] [RetinaNet] [DCN]