The paper presents a novel visual quality metric for lossy compressed video quality assessment. High degree of correlation with subjective estimations of quality is due to using of a convolutional neural network trained on a large amount of pairs video sequence-subjective quality score. We demonstrate how our predicted no-reference quality metric correlates with qualitative opinion in a human observer study. Results are shown on the EVVQ dataset with comparison existing approaches.
In the paper we propose approach for lossless image compression. Proposed method is based on separate processing of two image components: structure and texture. In the subsequent step separated components are compressed by standard RLE/LZW coding. We have performed a comparative analysis with existing techniques using standard test images. Our approach have shown promising results.
Content–based image retrieval systems have plenty of applications in modern world. The most important one is the image search by query image or by semantic description. Approaches to this problem are employed in personal photo–collection management systems, web–scale image search engines, medical systems, etc. Automatic analysis of large unlabeled image datasets is virtually impossible without satisfactory image–retrieval technique. It’s the main reason why this kind of automatic image processing has attracted so much attention during recent years. Despite rather huge progress in the field, semantically meaningful image retrieval still remains a challenging task. The main issue here is the demand to provide reliable results in short amount of time. This paper addresses the problem by novel technique for simultaneous learning of global image features and binary hash codes. Our approach provide mapping of pixel–based image representation to hash–value space simultaneously trying to save as much of semantic image content as possible. We use deep learning methodology to generate image description with properties of similarity preservation and statistical independence. The main advantage of our approach in contrast to existing is ability to fine–tune retrieval procedure for very specific application which allow us to provide better results in comparison to general techniques. Presented in the paper framework for data– dependent image hashing is based on use two different kinds of neural networks: convolutional neural networks for image description and autoencoder for feature to hash space mapping. Experimental results confirmed that our approach has shown promising results in compare to other state–of–the–art methods.
This paper proposes a video stabilization method using space-time video completion for effective static and dynamic textures reconstruction instead of frames cropping. The proposed method can produce full-frame videos by naturally filling in missing image parts by locally aligning image data of neighboring frames. We propose to use a set of descriptors that encapsulate the information of periodical motion of objects necessary to reconstruct missing/corrupted frames. The background is filled-in by extending spatial texture synthesis techniques using set of 3D patches. Experimental results demonstrate the effectiveness of the proposed method in the task of full-frame video stabilization.
Inpainting has received a lot of attention in recent years and quality assessment is an important task to evaluate different image reconstruction approaches. In many cases inpainting methods introduce a blur in sharp transitions in image and image contours in the recovery of large areas with missing pixels and often fail to recover curvy boundary edges. Quantitative metrics of inpainting results currently do not exist and researchers use human comparisons to evaluate their methodologies and techniques. Most objective quality assessment methods rely on a reference image, which is often not available in inpainting applications. Usually researchers use subjective quality assessment by human observers. It is difficult and time consuming procedure. This paper focuses on a machine learning approach for no-reference visual quality assessment for image inpainting based on the human visual property. Our method is based on observation that Local Binary Patterns well describe local structural information of the image. We use a support vector regression learned on assessed by human images to predict perceived quality of inpainted images. We demonstrate how our predicted quality value correlates with qualitative opinion in a human observer study. Results are shown on a human-scored dataset for different inpainting methods.
This article discusses features of the parallel hashing for the designing of the frame filtering tables in distributed computing systems. The proposed method of filtering tables design can reduce the time of frame processing by network bridges and switches and provide a low probability of filtering table overflowing. The optimal number of parallel tables was determined for a given amount of memory for table design.
This paper focuses on a machine learning approach for objective inpainting quality assessment. Inpainting has received a
lot of attention in recent years and quality assessment is an important task to evaluate different image reconstruction
approaches. Quantitative metrics for successful image inpainting currently do not exist; researchers instead are relying
upon qualitative human comparisons in order to evaluate their methodologies and techniques. We present an approach
for objective inpainting quality assessment based on natural image statistics and machine learning techniques. Our
method is based on observation that when images are properly normalized or transferred to a transform domain, local
descriptors can be modeled by some parametric distributions. The shapes of these distributions are different for noninpainted
and inpainted images. Approach permits to obtain a feature vector strongly correlated with a subjective image
perception by a human visual system. Next, we use a support vector regression learned on assessed by human images to
predict perceived quality of inpainted images. We demonstrate how our predicted quality value repeatably correlates
with a qualitative opinion in a human observer study.
This paper focuses on the fast texture and structure reconstruction of images. The proposed method, applied to images,
consists of several steps. The first one deals with the extracted textural features of the input images based on the
Law’s energy. The pixels around damaged image regions are clustered using these features, that allow to define the
correspondence between pixels from different patches. Second, cubic spline curve is applied to reconstruct a structure
and to connect edges and contours in the damaged area. The choice of the current pixel to be recovered is decided using
the fast marching approach. The Telea method or modifications of the exemplar based method are used after this
depending on the classification of the regions where to-be-restored pixel is located. In modification to quickly find
patches we use the perceptual hash. Such a strategy allows to get some data structure containing the hashes of similar
patches. This enables us to reduce the search procedure to the procedure for "calculations" of the patch. The proposed
method is tested on various samples of images, with different geometrical features and compared with the state-of-the-art
image inpainting methods; the proposed technique is shown to produce better results in reconstruction of missing small
and large objects on test images.
The problem of automatic video restoration and object removal attract the attention of many researchers. In this paper we
present a new framework for video inpainting. We consider the case when a camera motion is approximately parallel to
the plane of image projection. The scene may consist of a stationary background with a moving foreground, both of
which may require inpainting. Moving objects can move differently, but should not to change their size. A framework
presented in this paper contains the following steps: moving objects identification, moving objects tracking and
background/foreground segmentation, inpainting and, finally, a video rendering. Some results on test video sequence
processing are presented.