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The JPEG committee (formally, ISO SC29 WG1) is currently standardizing a lightweight mezzanine codec for video over IP transport under the name JPEG XS. A particular challenging design constraint of this codec is multi-generation robustness, that is the necessity to minimize the error built-up under multiple re-compression cycles. In this paper, we discuss the sources of such errors, how they are...
The utility to decode hand movement parameters is significant to the control of artificial limb in the BCI fields. Most previous studies have adopted amplitude features of the low-frequency EEG signals to decode hand movement parameters. In this study, we have investigated the instantaneous phase of the low-frequency EEG signals attained by Hilbert transform for such a task for the first time, and...
Existing methods for layer-based backward compatible high dynamic range (HDR) image and video coding mostly focus on the rate-distortion optimization of base layer while neglecting the encoding of the residue signal in the enhancement layer. Although some recent studies handle residue coding by designing function based fixed global mapping curves for 8-bit conversion and exploiting standard codecs...
In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network with an expert-designed generative model that serves as decoder. The core innovation is the differentiable parametric decoder that encapsulates image...
Human face exhibits an inherent hierarchy in its representations (i.e., holistic facial expressions can be encoded via a set of facial action units (AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown great results in unsupervised extraction of hierarchical latent representations from large amounts of image data, while being robust to noise and other undesired artifacts. Potentially,...
Shape reconstruction techniques using structured light have been widely researched and developed due to their robustness, high precision, and density. Because the techniques are based on decoding a pattern to find correspondences, it implicitly requires that the projected patterns be clearly captured by an image sensor, i.e., to avoid defocus and motion blur of the projected pattern. Although intensive...
Recently, the community of style transfer is trying to incorporate semantic information into traditional system. This practice achieves better perceptual results by transferring the style between semantically-corresponding regions. Yet, few efforts are invested to address the computation bottleneck of back-propagation. In this paper, we propose a new framework for fast semantic style transfer. Our...
Compressed sensing (CS) has drawn many interest in the field ultrasound (US) image recovery. It has demonstrated promising results in the recovery of radio-frequency element raw-data [Liebgott et. al. ULTRAS13, Besson et. al. SPARS17]. The objective of such approaches is to recover the raw-data from undersampled random measurements. It is achieved by means of convex optimization or greedy methods...
This Paper proposes a Novel frame work for reversible data hiding scheme over an encrypted cover medium. Reversible data hiding techniques are gaining huge importance in recent years due to its excellent feature of enabling zero distortion reconstruction of both cover medium information as well as secret data information, while protecting the confidentiality of the payload image information. We propose...
This paper proposes a new high dimensional regression method by merging Gaussian process regression into a variational autoencoder framework. In contrast to other regression methods, the proposed method focuses on the case where output responses are on a complex high dimensional manifold, such as images. Our contributions are summarized as follows: (i) A new regression method estimating high dimensional...
This paper presents a set of full-resolution lossy image compression methods based on neural networks. Each of the architectures we describe can provide variable compression rates during deployment without requiring retraining of the network: each network need only be trained once. All of our architectures consist of a recurrent neural network (RNN)-based encoder and decoder, a binarizer, and a neural...
Existing zero-shot learning (ZSL) models typically learn a projection function from a feature space to a semantic embedding space (e.g. attribute space). However, such a projection function is only concerned with predicting the training seen class semantic representation (e.g. attribute prediction) or classification. When applied to test data, which in the context of ZSL contains different (unseen)...
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure extraction as a pixel-wise segmentation task, and propose a unified model that classifies pixels based not only on their visual appearance, as in the traditional page segmentation task, but also on the content of underlying text. Moreover,...
We present a method for synthesizing a frontal, neutral-expression image of a persons face, given an input face photograph. This is achieved by learning to generate facial landmarks and textures from features extracted from a facial-recognition network. Unlike previous generative approaches, our encoding feature vector is largely invariant to lighting, pose, and facial expression. Exploiting this...
We study the problem of holistic scene understanding. We would like to obtain a compact, expressive, and interpretable representation of scenes that encodes information such as the number of objects and their categories, poses, positions, etc. Such a representation would allow us to reason about and even reconstruct or manipulate elements of the scene. Previous works have used encoder-decoder based...
Most of the conventional face hallucination methods assume the input image is sufficiently large and aligned, and all require the input image to be noise-free. Their performance degrades drastically if the input image is tiny, unaligned, and contaminated by noise. In this paper, we introduce a novel transformative discriminative autoencoder to 8X super-resolve unaligned noisy and tiny (16X16) low-resolution...
In this paper, we propose an effective face completion algorithm using a deep generative model. Different from well-studied background completion, the face completion task is more challenging as it often requires to generate semantically new pixels for the missing key components (e.g., eyes and mouths) that contain large appearance variations. Unlike existing nonparametric algorithms that search for...
Deep Auto-Encoder (DAE) has shown its promising power in high-level representation learning. From the perspective of manifold learning, we propose a graph regularized deep neural network (GR-DNN) to endue traditional DAEs with the ability of retaining local geometric structure. A deep-structured regularizer is formulated upon multi-layer perceptions to capture this structure. The robust and discriminative...
Compression of multispectal images is of great importance in an environment where resources such as computational power and memory are scarce. To that end, we propose a new extremely low-complexity encoding approach for compression of multispectral images, that shifts the complexity to the decoding. Our method combines principles from compressed sensing and distributed source coding. Specifically,...
In this paper a robust encoding scheme is proposed to improve the visual quality of HEVC decoded video when intra frames are lost along the streaming path. For this purpose, the encoding process includes frame loss simulation and subsequent error concealment, to find the most efficient method that should be used by a decoder to recover lost intra frames. In this novel scheme, each image is divided...
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