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ROI-based Deep Image Compression with Swin Transformers
Encoding the Region Of Interest (ROI) with better quality than the background has many applications including video conferencing systems, video surveillance and object-oriented vision tasks.
Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering
Based on this observation, we design an asymmetric paradigm, in which the encoder employs three stages of MSRBs to improve the learning capacity, whereas the decoder only needs one stage of MSRB to yield satisfactory reconstruction, thereby reducing the decoding complexity without sacrifcing performance.
Reproducing Kernels and New Approaches in Compositional Data Analysis
Compositional data, such as human gut microbiomes, consist of non-negative variables whose only the relative values to other variables are available.
Skeleton-based Approaches based on Machine Vision: A Survey
Recently, skeleton-based approaches have achieved rapid progress on the basis of great success in skeleton representation.
Deep Learning-based Image Compression with Trellis Coded Quantization
Recently many works attempt to develop image compression models based on deep learning architectures, where the uniform scalar quantizer (SQ) is commonly applied to the feature maps between the encoder and decoder.
