23,320 research outputs found
Deep perceptual preprocessing for video coding
We introduce the concept of rate-aware deep perceptual preprocessing (DPP) for video encoding. DPP makes a single pass over each input frame in order to enhance its visual quality when the video is to be compressed with any codec at any bitrate. The resulting bitstreams can be decoded and displayed at the client side without any post-processing component. DPP comprises a convolutional neural network that is trained via a composite set of loss functions that incorporates: (i) a perceptual loss based on a trained no-reference image quality assessment model, (ii) a reference-based fidelity loss expressing L1 and structural similarity aspects, (iii) a motion-based rate loss via block-based transform, quantization and entropy estimates that converts the essential components of standard hybrid video encoder designs into a trainable framework. Extensive testing using multiple quality metrics and AVC, AV1 and VVC encoders shows that DPP+encoder reduces, on average, the bitrate of the corresponding encoder by 11%. This marks the first time a server-side neural processing component achieves such savings over the state-of-the-art in video coding
Extracting textual overlays from social media videos using neural networks
Textual overlays are often used in social media videos as people who watch
them without the sound would otherwise miss essential information conveyed in
the audio stream. This is why extraction of those overlays can serve as an
important meta-data source, e.g. for content classification or retrieval tasks.
In this work, we present a robust method for extracting textual overlays from
videos that builds up on multiple neural network architectures. The proposed
solution relies on several processing steps: keyframe extraction, text
detection and text recognition. The main component of our system, i.e. the text
recognition module, is inspired by a convolutional recurrent neural network
architecture and we improve its performance using synthetically generated
dataset of over 600,000 images with text prepared by authors specifically for
this task. We also develop a filtering method that reduces the amount of
overlapping text phrases using Levenshtein distance and further boosts system's
performance. The final accuracy of our solution reaches over 80A% and is au
pair with state-of-the-art methods.Comment: International Conference on Computer Vision and Graphics (ICCVG) 201
Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning
Computer-generated graphics (CGs) are images generated by computer software.
The~rapid development of computer graphics technologies has made it easier to
generate photorealistic computer graphics, and these graphics are quite
difficult to distinguish from natural images (NIs) with the naked eye. In this
paper, we propose a method based on sensor pattern noise (SPN) and deep
learning to distinguish CGs from NIs. Before being fed into our convolutional
neural network (CNN)-based model, these images---CGs and NIs---are clipped into
image patches. Furthermore, three high-pass filters (HPFs) are used to remove
low-frequency signals, which represent the image content. These filters are
also used to reveal the residual signal as well as SPN introduced by the
digital camera device. Different from the traditional methods of distinguishing
CGs from NIs, the proposed method utilizes a five-layer CNN to classify the
input image patches. Based on the classification results of the image patches,
we deploy a majority vote scheme to obtain the classification results for the
full-size images. The~experiments have demonstrated that (1) the proposed
method with three HPFs can achieve better results than that with only one HPF
or no HPF and that (2) the proposed method with three HPFs achieves 100\%
accuracy, although the NIs undergo a JPEG compression with a quality factor of
75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296;
Sensors 2018, 18(4), 129
Learning Blind Motion Deblurring
As handheld video cameras are now commonplace and available in every
smartphone, images and videos can be recorded almost everywhere at anytime.
However, taking a quick shot frequently yields a blurry result due to unwanted
camera shake during recording or moving objects in the scene. Removing these
artifacts from the blurry recordings is a highly ill-posed problem as neither
the sharp image nor the motion blur kernel is known. Propagating information
between multiple consecutive blurry observations can help restore the desired
sharp image or video. Solutions for blind deconvolution based on neural
networks rely on a massive amount of ground-truth data which is hard to
acquire. In this work, we propose an efficient approach to produce a
significant amount of realistic training data and introduce a novel recurrent
network architecture to deblur frames taking temporal information into account,
which can efficiently handle arbitrary spatial and temporal input sizes. We
demonstrate the versatility of our approach in a comprehensive comparison on a
number of challening real-world examples.Comment: International Conference on Computer Vision (ICCV) (2017
Baseline CNN structure analysis for facial expression recognition
We present a baseline convolutional neural network (CNN) structure and image
preprocessing methodology to improve facial expression recognition algorithm
using CNN. To analyze the most efficient network structure, we investigated
four network structures that are known to show good performance in facial
expression recognition. Moreover, we also investigated the effect of input
image preprocessing methods. Five types of data input (raw, histogram
equalization, isotropic smoothing, diffusion-based normalization, difference of
Gaussian) were tested, and the accuracy was compared. We trained 20 different
CNN models (4 networks x 5 data input types) and verified the performance of
each network with test images from five different databases. The experiment
result showed that a three-layer structure consisting of a simple convolutional
and a max pooling layer with histogram equalization image input was the most
efficient. We describe the detailed training procedure and analyze the result
of the test accuracy based on considerable observation.Comment: 6 pages, RO-MAN2016 Conferenc
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