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We present a per-channel framework for extending monochrome barcodes to color for display applications, offering increased data rates and capacity. Data is independently encoded into barcodes, incorporated within a color image as the red (R), green (G), and blue (B) channels and decoded from the corresponding R, G, and B channels in the image of the displayed barcode captured with a smartphone. Using...
Depth maps are typically made of smooth regions separated by sharp edges. Following this rationale, this paper presents a novel coding scheme where depth data is represented by a set of contours defining the various regions together with a compact representation of the values inside each region. The proposed coding scheme is based on elastic curves, which make possible to compactly represent the contours...
In intra video coding, intra frames are predicted with intra prediction and the prediction residual signal is encoded. In many transform-based video coding systems, intra prediction residuals are encoded with transforms. For example, the Discrete Cosine Transform (DCT) and the Asymmetric Discrete Sine Transform (ADST) are used for intra prediction residuals in many coding systems. In the recent work,...
Lossless image coding process predicts the value of current pixel from previously decoded pixel values. Then the prediction error is classified according to the context model. This classification splits the sources with different distributions and hence reduce the total entropy of the prediction error signals. In the literature, the predictor has been intensively studied. Some evolutionary approaches...
Our challenge is the design of a “universal” bit-efficient image compression approach. The prime goal is to allow reconstruction of images with high quality. In addition, we attempt to design the coder and decoder “universal”, such that MPEG-7-like low-and mid-level descriptors are an integral part of the coded representation. To this end, we introduce a sparse Mixture-of-Experts regression approach...
In image compression, block-based transforms tend to be inefficient when blocks contain arbitrarily shaped discontinuities. For this reason, transforms incorporating directional information are an appealing alternative. Starting from the graph Fourier transform, in this paper we present a new transform, called Subspace-Sparsifying Steer-able DCT, that can be obtained by rotating the basis vectors...
The paper discusses the use of existing metrics, such as HDR-VDP and extensions of MS-SSIM and PSNR, for prediction of quality in high dynamic range (HDR) images and video. The discussion is based on the experience in using those metrics to evaluate and improve image compression for the new JPEG XT standard, and video compression for the LumaHDR open source codec. The paper explains why existing non-HDR...
Directly connected to the texture appearance, texture granularity is an effective measurement for geographic resources classification, product quality monitoring and image compression ratio selection. However, the application of existing works on texture granularity is limited by intense computation and the dependence on empirically selected parameters that vary among different textures. This paper...
Compressive imagers, e.g. the single-pixel camera (SPC), acquire measurements in the form of random projections of the scene instead of pixel intensities. Compressive Sensing (CS) theory allows accurate reconstruction of the image even from a small number of such projections. However, in practice, most reconstruction algorithms perform poorly at low measurement rates and are computationally very expensive...
A label consistent recursive least squares dictionary learning algorithm, LC-RLSDLA, is proposed to learn discriminative dictionaries for image classification based on sparse coding. The class label information and a label consistency term are used in the cost function to enforce discriminability among the sparse codes. Two operation modes are derived for the LC-RLSDLA: the supervised learning mode,...
In this paper, we propose a novel single image super-resolution (SR) method based on low-rank sparse representation with self-similarity learning. Sparse representation is known as a promising method for SR. However, the sparse codes for low resolution (LR) patches gained by conventional method are not faithful to those for the original high resolution (HR) ones. To overcome this defect, we explore...
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The upcoming JPEG XT standard for High Dynamic Range (HDR) images defines a common framework for the lossy and lossless representation of high-dynamic range images. It describes the decoding process as the combination of various processing tools that can be combined freely. In this paper we analyze the coding efficiency of different decoding tools through a large scale objective quality testing using...
This paper studies the influence of JPEG-XT on LDR generation using TMOs'. JPEG-XT encodes HDR images into a two layer scheme, encoding a LDR version of the image in a base layer, and the residual HDR information in an enhancement layer. The question addressed here is to understand if this model allows to extract a new LDR representation using a different TMO, independently of the TMO used to generate...
Ultrasonography is an important tool and has been widely used in clinical applications, however, the physicians and surgeons still often suffers great difficulties in diagnosis and treatment due to the high speckle noise of ultrasound images. Existing speckle reduction methods usually over-smooth low contrast features since they are sensitive to contrast variations in the images. In our paper, we...
In this paper we present a novel idea of evaluating similarity between two images aided by a salient object detection framework. For computing similarity between images consisting of multiple objects and varying background, extracting features relevant to the object of interest is of cruicial importance. To accomplish this task, we employ a saliency guided dictionary learning framework for image similarity...
With the recent improvements in 3-D capture technologies for applications such as virtual reality, preserving cultural artifacts, and mobile mapping systems, new methods for compressing 3-D point cloud representations are needed to reduce the amount of bandwidth or storage consumed. For point clouds having attributes such as color associated with each point, several existing methods perform attribute...
Existing face hallucination methods are optimized to super-resolve uncompressed images and are not able to handle the distortions caused by compression. This work presents a new dictionary construction method which jointly models both distortions caused by down-sampling and compression. The resulting dictionaries are then used to make three face super-resolution methods more robust to compression...
Image classification is a general visual analysis task based on the image content coded by its representation. In this research, we proposed an image representation method that is based on the perceptual shape features and their spatial distributions. A natural language processing concept, N-gram, is adopted to generate a set of perceptual shape visual words for encoding image features. By combining...
The latest video compression standard HEVC sets new benchmarks concerning the efficiency for both video coding and also still image coding, i.e., pure intra picture coding. Nevertheless, its high complexity created by the rate-distortion optimization procedure is a serious drawback. To reduce this computational burden, several algorithms for fast mode decision have been proposed. However, most of...
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