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Found 14 papers, 4 papers with code
A Neural-network Enhanced Video Coding Framework beyond ECM
In this paper, a hybrid video compression framework is proposed that serves as a demonstrative showcase of deep learning-based approaches extending beyond the confines of traditional coding methodologies.
SpikeCodec: An End-to-end Learned Compression Framework for Spiking Camera
Recently, the bio-inspired spike camera with continuous motion recording capability has attracted tremendous attention due to its ultra high temporal resolution imaging characteristic.
Machine Perception-Driven Image Compression: A Layered Generative Approach
In this age of information, images are a critical medium for storing and transmitting information.
Learning to Compress Unmanned Aerial Vehicle (UAV) Captured Video: Benchmark and Analysis
During the past decade, the Unmanned-Aerial-Vehicles (UAVs) have attracted increasing attention due to their flexible, extensive, and dynamic space-sensing capabilities.
Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling
Each score is derived from machine perceptual differences between original and compressed images.
Cross Modal Compression: Towards Human-comprehensible Semantic Compression
With the recent advances in cross modal translation and generation, in this paper, we propose the cross modal compression~(CMC), a semantic compression framework for visual data, to transform the high redundant visual data~(such as image, video, etc.)
Rate Distortion Characteristic Modeling for Neural Image Compression
End-to-end optimized neural image compression (NIC) has obtained superior lossy compression performance recently.
Visual Analysis Motivated Rate-Distortion Model for Image Coding
We first propose the region of interest for machine (ROIM) to evaluate the degree of importance for each coding tree unit (CTU) in visual analysis.
Thousand to One: Semantic Prior Modeling for Conceptual Coding
To this end, we propose a novel end-to-end semantic prior modeling-based conceptual coding scheme towards extremely low bitrate image compression, which leverages semantic-wise deep representations as a unified prior for entropy estimation and texture synthesis.
Conceptual Compression via Deep Structure and Texture Synthesis
To this end, we propose a novel conceptual compression framework that encodes visual data into compact structure and texture representations, then decodes in a deep synthesis fashion, aiming to achieve better visual reconstruction quality, flexible content manipulation, and potential support for various vision tasks.
Universal Adversarial Perturbations Generative Network for Speaker Recognition
Attacking deep learning based biometric systems has drawn more and more attention with the wide deployment of fingerprint/face/speaker recognition systems, given the fact that the neural networks are vulnerable to the adversarial examples, which have been intentionally perturbed to remain almost imperceptible for human.
Direct Speech-to-image Translation
In this paper, we attempt to translate the speech signals into the image signals without the transcription stage.
Multimedia Sound Audio and Speech Processing
Image and Video Compression with Neural Networks: A Review
Deep convolution neural network (CNN) which makes the neural network resurge in recent years and has achieved great success in both artificial intelligent and signal processing fields, also provides a novel and promising solution for image and video compression.
Spatial-Temporal Residue Network Based In-Loop Filter for Video Coding
Deep learning has demonstrated tremendous break through in the area of image/video processing.
Multimedia
