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Found 13 papers, 3 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.
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.
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.
Advanced Super-Resolution using Lossless Pooling Convolutional Networks
In this paper, we present a novel deep learning-based approach for still image super-resolution, that unlike the mainstream models does not rely solely on the input low resolution image for high quality upsampling, and takes advantage of a set of artificially created auxiliary self-replicas of the input image that are incorporated in the neural network to create an enhanced and accurate upscaling scheme.
Convolutional Neural Networks for Video Quality Assessment
Moreover, transmission also introduces delays and other distortion types which affect the perceived quality.
QCBA: Improving Rule Classifiers Learned from Quantitative Data by Recovering Information Lost by Discretisation
A prediscretisation of numerical attributes which is required by some rule learning algorithms is a source of inefficiencies.
Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning
Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement.
Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning
Aiming at improving performance of visual classification in a cost-effective manner, this paper proposes an incremental semi-supervised learning paradigm called Deep Co-Space (DCS).
CUNet: A Compact Unsupervised Network for Image Classification
In this paper, we propose a compact network called CUNet (compact unsupervised network) to counter the image classification challenge.
Approximated Robust Principal Component Analysis for Improved General Scene Background Subtraction
This article addresses a few critical issues including: embedding global motion parameters in the matrix decomposition model, i. e., estimation of global motion parameters simultaneously with the foreground/background separation task, considering matrix block-sparsity rather than generic matrix sparsity as natural feature in video processing applications, attenuating background ghosting effects when foreground is subtracted, and more critically providing an extremely efficient algorithm to solve the low-rank/sparse matrix decomposition task.
Reflection Invariance: an important consideration of image orientation
In this position paper, we consider the state of computer vision research with respect to invariance to the horizontal orientation of an image -- what we term reflection invariance.
