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Found 21 papers, 8 papers with code
Efficient Convolution and Transformer-Based Network for Video Frame Interpolation
This network reduces the memory burden by close to 50% and runs up to four times faster during inference time compared to existing transformer-based interpolation methods.
Query-based Video Summarization with Pseudo Label Supervision
Existing datasets for manually labelled query-based video summarization are costly and thus small, limiting the performance of supervised deep video summarization models.
PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based Network
In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos.
Slimmable Video Codec
Neural video compression has emerged as a novel paradigm combining trainable multilayer neural networks and machine learning, achieving competitive rate-distortion (RD) performances, but still remaining impractical due to heavy neural architectures, with large memory and computational demands.
Multi-encoder Network for Parameter Reduction of a Kernel-based Interpolation Architecture
Despite all the benefits interpolation methods offer, many of these networks require a lot of parameters, with more parameters meaning a heavier computational burden.
Complexity Reduction of Learned In-Loop Filtering in Video Coding
In video coding, in-loop filters are applied on reconstructed video frames to enhance their perceptual quality, before storing the frames for output.
DCNGAN: A Deformable Convolutional-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video
Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos.
DVC-P: Deep Video Compression with Perceptual Optimizations
In this paper, we introduce deep video compression with perceptual optimizations (DVC-P), which aims at increasing perceptual quality of decoded videos.
Improved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding
The approach requires a single neural network to be trained from which a full quarter-pixel interpolation filter set is derived, as the network is easily interpretable due to its linear structure.
Towards Transparent Application of Machine Learning in Video Processing
Machine learning techniques for more efficient video compression and video enhancement have been developed thanks to breakthroughs in deep learning.
Attention-based Stylisation for Exemplar Image Colourisation
Stylised outputs are then obtained by computing similarities between both feature representations in order to transfer the style of the reference to the content of the target input.
GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization
Traditional video summarization methods generate fixed video representations regardless of user interest.
DANICE: Domain adaptation without forgetting in neural image compression
Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data.
Attention-Based Neural Networks for Chroma Intra Prediction in Video Coding
Simplifications include a framework for reducing the overhead of the convolutional operations, a simplified cross-component processing model integrated into the original architecture, and a methodology to perform integer-precision approximations with the aim to obtain fast and hardware-aware implementations.
Chroma Intra Prediction with attention-based CNN architectures
Neural networks can be used in video coding to improve chroma intra-prediction.
Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding
Deep learning has shown great potential in image and video compression tasks.
Analytic Simplification of Neural Network based Intra-Prediction Modes for Video Compression
With the increasing demand for video content at higher resolutions, it is evermore critical to find ways to limit the complexity of video encoding tasks in order to reduce costs, power consumption and environmental impact of video services.
Estimation of Rate Control Parameters for Video Coding Using CNN
As reference frames are essential for exploiting temporal redundancies, intra frames are usually assigned a larger portion of the available bits.
End-to-End Conditional GAN-based Architectures for Image Colourisation
In this work recent advances in conditional adversarial networks are investigated to develop an end-to-end architecture based on Convolutional Neural Networks (CNNs) to directly map realistic colours to an input greyscale image.
Decision Trees for Complexity Reduction in Video Compression
This paper proposes a method for complexity reduction in practical video encoders using multiple decision tree classifiers.
Convolutional Neural Networks for Video Quality Assessment
Moreover, transmission also introduces delays and other distortion types which affect the perceived quality.
