3,759 research outputs found
Stochastic Modeling and Resolution-Free Rendering of Film Grain
The realistic synthesis and rendering of film grain is a crucial goal for many amateur and professional photographers and film-makers whose artistic works require the authentic feel of analog photography. The objective of this work is to propose an algorithm that reproduces the visual aspect of film grain texture on any digital image. Previous approaches to this problem either propose unrealistic models or simply blend scanned images of film grain with the digital image, in which case the result is inevitably limited by the quality and resolution of the initial scan. In this work, we introduce a stochastic model to approximate the physical reality of film grain, and propose a resolution-free rendering algorithm to simulate realistic film grain for any digital input image. By varying the parameters of this model, we can achieve a wide range of grain types. We demonstrate this by comparing our results with film grain examples from dedicated software, and show that our rendering results closely resemble these real film emulsions. In addition to realistic grain rendering, our resolution-free algorithm allows for any desired zoom factor, even down to the scale of the microscopic grains themselves
Optimizing Image Compression via Joint Learning with Denoising
High levels of noise usually exist in today's captured images due to the
relatively small sensors equipped in the smartphone cameras, where the noise
brings extra challenges to lossy image compression algorithms. Without the
capacity to tell the difference between image details and noise, general image
compression methods allocate additional bits to explicitly store the undesired
image noise during compression and restore the unpleasant noisy image during
decompression. Based on the observations, we optimize the image compression
algorithm to be noise-aware as joint denoising and compression to resolve the
bits misallocation problem. The key is to transform the original noisy images
to noise-free bits by eliminating the undesired noise during compression, where
the bits are later decompressed as clean images. Specifically, we propose a
novel two-branch, weight-sharing architecture with plug-in feature denoisers to
allow a simple and effective realization of the goal with little computational
cost. Experimental results show that our method gains a significant improvement
over the existing baseline methods on both the synthetic and real-world
datasets. Our source code is available at
https://github.com/felixcheng97/DenoiseCompression.Comment: Accepted to ECCV 202
Encoding in the Dark Grand Challenge:An Overview
A big part of the video content we consume from video providers consists of
genres featuring low-light aesthetics. Low light sequences have special
characteristics, such as spatio-temporal varying acquisition noise and light
flickering, that make the encoding process challenging. To deal with the
spatio-temporal incoherent noise, higher bitrates are used to achieve high
objective quality. Additionally, the quality assessment metrics and methods
have not been designed, trained or tested for this type of content. This has
inspired us to trigger research in that area and propose a Grand Challenge on
encoding low-light video sequences. In this paper, we present an overview of
the proposed challenge, and test state-of-the-art methods that will be part of
the benchmark methods at the stage of the participants' deliverable assessment.
From this exploration, our results show that VVC already achieves a high
performance compared to simply denoising the video source prior to encoding.
Moreover, the quality of the video streams can be further improved by employing
a post-processing image enhancement method
Artificial Intelligence in the Creative Industries: A Review
This paper reviews the current state of the art in Artificial Intelligence
(AI) technologies and applications in the context of the creative industries. A
brief background of AI, and specifically Machine Learning (ML) algorithms, is
provided including Convolutional Neural Network (CNNs), Generative Adversarial
Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement
Learning (DRL). We categorise creative applications into five groups related to
how AI technologies are used: i) content creation, ii) information analysis,
iii) content enhancement and post production workflows, iv) information
extraction and enhancement, and v) data compression. We critically examine the
successes and limitations of this rapidly advancing technology in each of these
areas. We further differentiate between the use of AI as a creative tool and
its potential as a creator in its own right. We foresee that, in the near
future, machine learning-based AI will be adopted widely as a tool or
collaborative assistant for creativity. In contrast, we observe that the
successes of machine learning in domains with fewer constraints, where AI is
the `creator', remain modest. The potential of AI (or its developers) to win
awards for its original creations in competition with human creatives is also
limited, based on contemporary technologies. We therefore conclude that, in the
context of creative industries, maximum benefit from AI will be derived where
its focus is human centric -- where it is designed to augment, rather than
replace, human creativity
Signal processing for high-definition television
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1995.Includes bibliographical references (p. 60-62).by Peter Monta.Ph.D
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ReSCon '09, Research Student Conference: Book of Abstracts
The second SED Research Student Conference (ReSCon2009) was hosted over three days, 22-24 June 2009, in the Lecture Centre at Brunel University. The conference consisted of technical presentations, a poster session and social events. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School of Engineering and Design at Brunel University. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School
Index to NASA Tech Briefs, January - June 1967
Technological innovations for January-June 1967, abstracts and subject inde
Digital watermarking and novel security devices
EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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