13,476 research outputs found
Rate-Distortion Analysis of Multiview Coding in a DIBR Framework
Depth image based rendering techniques for multiview applications have been
recently introduced for efficient view generation at arbitrary camera
positions. Encoding rate control has thus to consider both texture and depth
data. Due to different structures of depth and texture images and their
different roles on the rendered views, distributing the available bit budget
between them however requires a careful analysis. Information loss due to
texture coding affects the value of pixels in synthesized views while errors in
depth information lead to shift in objects or unexpected patterns at their
boundaries. In this paper, we address the problem of efficient bit allocation
between textures and depth data of multiview video sequences. We adopt a
rate-distortion framework based on a simplified model of depth and texture
images. Our model preserves the main features of depth and texture images.
Unlike most recent solutions, our method permits to avoid rendering at encoding
time for distortion estimation so that the encoding complexity is not
augmented. In addition to this, our model is independent of the underlying
inpainting method that is used at decoder. Experiments confirm our theoretical
results and the efficiency of our rate allocation strategy
Hierarchy Composition GAN for High-fidelity Image Synthesis
Despite the rapid progress of generative adversarial networks (GANs) in image
synthesis in recent years, the existing image synthesis approaches work in
either geometry domain or appearance domain alone which often introduces
various synthesis artifacts. This paper presents an innovative Hierarchical
Composition GAN (HIC-GAN) that incorporates image synthesis in geometry and
appearance domains into an end-to-end trainable network and achieves superior
synthesis realism in both domains simultaneously. We design an innovative
hierarchical composition mechanism that is capable of learning realistic
composition geometry and handling occlusions while multiple foreground objects
are involved in image composition. In addition, we introduce a novel attention
mask mechanism that guides to adapt the appearance of foreground objects which
also helps to provide better training reference for learning in geometry
domain. Extensive experiments on scene text image synthesis, portrait editing
and indoor rendering tasks show that the proposed HIC-GAN achieves superior
synthesis performance qualitatively and quantitatively.Comment: 11 pages, 8 figure
Hierarchical Hole-filling For Depth-based View Synthesis In Ftv And 3d Video
Methods for hierarchical hole-filling and depth adaptive hierarchical hole-filling and error correcting in 2D images, 3D images, and 3D wrapped images are provided. Hierarchical hole-filling can comprise reducing an image that contains holes, expanding the reduced image, and filling the holes in the image with data obtained from the expanded image. Depth adaptive hierarchical hole-filling can comprise preprocessing the depth map of a 3D wrapped image that contains holes, reducing the preprocessed image, expanding the reduced image, and filling the holes in the 3D wrapped image with data obtained from the expanded image. These methods are can efficiently reduce errors in images and produce 3D images from a 2D images and/or depth map information.Georgia Tech Research Corporatio
Characterizing and Improving Stability in Neural Style Transfer
Recent progress in style transfer on images has focused on improving the
quality of stylized images and speed of methods. However, real-time methods are
highly unstable resulting in visible flickering when applied to videos. In this
work we characterize the instability of these methods by examining the solution
set of the style transfer objective. We show that the trace of the Gram matrix
representing style is inversely related to the stability of the method. Then,
we present a recurrent convolutional network for real-time video style transfer
which incorporates a temporal consistency loss and overcomes the instability of
prior methods. Our networks can be applied at any resolution, do not re- quire
optical flow at test time, and produce high quality, temporally consistent
stylized videos in real-time
Optimization of Occlusion-Inducing Depth Pixels in 3-D Video Coding
The optimization of occlusion-inducing depth pixels in depth map coding has
received little attention in the literature, since their associated texture
pixels are occluded in the synthesized view and their effect on the synthesized
view is considered negligible. However, the occlusion-inducing depth pixels
still need to consume the bits to be transmitted, and will induce geometry
distortion that inherently exists in the synthesized view. In this paper, we
propose an efficient depth map coding scheme specifically for the
occlusion-inducing depth pixels by using allowable depth distortions. Firstly,
we formulate a problem of minimizing the overall geometry distortion in the
occlusion subject to the bit rate constraint, for which the depth distortion is
properly adjusted within the set of allowable depth distortions that introduce
the same disparity error as the initial depth distortion. Then, we propose a
dynamic programming solution to find the optimal depth distortion vector for
the occlusion. The proposed algorithm can improve the coding efficiency without
alteration of the occlusion order. Simulation results confirm the performance
improvement compared to other existing algorithms
An efficient technique of texture representation in segmentation-based image coding schemes
In segmentation-based image coding techniques the image to be compressed is first segmented. Then, the information is coded describing the shape and the interior of the regions. A new method to encode the texture obtained in segmentation-based coding schemes is presented. The approach combines 2-D linear prediction and stochastic vector quantization. To encode a texture, a linear predictor is computed first. Next, a codebook following the prediction error model is generated and the prediction error is encoded with VQ. In the decoder, the error image is decoded first and then filtered as a whole, using the prediction filter. Hence, correlation between pixels is not lost from one block to another and a good reproduction quality can be achieved.Peer ReviewedPostprint (published version
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