DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
Explore this paper's citation graph
- Type
- preprint
- Published
- 2018-06-08
- Cited by
- 107
- References
- 27
- Access
- Open access
- OpenAlex
- https://openalex.org/W2805124033
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:47020766
Keywords
Computer science, Artificial intelligence, Computer vision, Image segmentation, Segmentation
References
- Multiscale structural similarity for image quality assessment
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- A robust, scalable, object-based video compression technique for very low bit-rate coding
- Image quality assessment: from error visibility to structural similarity
- The Cityscapes Dataset for Semantic Urban Scene Understanding
- Full Resolution Image Compression with Recurrent Neural Networks
- FLIF: Free lossless image format based on MANIAC compression
- Pyramid Scene Parsing Network
- Loss Functions for Image Restoration With Neural Networks
- Lossy Image Compression with Compressive Autoencoders
- Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
- Scene Parsing through ADE20K Dataset
- Generative Adversarial Networks for Extreme Learned Image Compression
- Generative Compression
- Joint Autoregressive and Hierarchical Priors for Learned Image Compression
- Extreme Learned Image Compression with GANs
- High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
- Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
- Real-Time Adaptive Image Compression
- Towards Image Understanding from Deep Compression without Decoding
Cited by
- Color image segmentation using genetic algorithm with aggregation-based clustering validity index (CVI)
- Deep Learning-Based Video Coding
- Improved Hybrid Layered Image Compression using Deep Learning and Traditional Codecs
- Learned Image Compression with Residual Coding
- Multi-Channel Multi-Loss Deep Learning Based Compression Model for Color Images
- Learned Variable-Rate Image Compression With Residual Divisive Normalization
- Optimized Latent Features for Deep Image Compression
- Deep Learning-Based Image Compression with Trellis Coded Quantization
- Generalized Octave Convolutions for Learned Multi-Frequency Image Compression
- Distributed Learning and Inference With Compressed Images
- An Extended Hybrid Image Compression Based on Soft-to-Hard Quantification
- JPAD-SE: High-Level Semantics for Joint Perception-Accuracy-Distortion Enhancement in Image Compression
- Image Compression with Encoder-Decoder Matched Semantic Segmentation
- A Hybrid Image Codec with Learned Residual Coding
- Nonlinear Transform Coding
- Different Approaches for Semantic Segmentation
- Image Coding With Data-Driven Transforms: Methodology, Performance and Potential
- Conceptual Compression via Deep Structure and Texture Synthesis
- Deep semantic segmentation-based multiple description coding
- A Hybrid Layered Image Compressor with Deep-Learning Technique
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