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Image compression plays more and more important role in image processing. Image sparse coding with learned over-complete dictionaries shows promising results on image compression by representing images with dictionary atoms compactly. Within the sparse coding based compression framework, a sparse dictionary is first learned from training images in a predefined image library, and then an image is compressed...
Traditional digital display devices, due to their hardware limitations, cannot represent the whole range of luminance in High Dynamic Range (HDR) images. In order to solve this incompatible problem, many tone mapping techniques were introduced to reproduce HDR images presently. Unlike one of the traditional work of art in [13], this paper proposes a fast and multi-scale decomposition based tone mapping...
Geometry images with normal data are a regular representation for approximating realistic 3D meshes, which can be compressed by image codec algorithms. However normal data include too much detailed information, which makes traditional normal images difficult to be compressed efficiently. In this paper, we first propose angle-normal images to reduce the number of normal component channels from three...
Linear representation models are effective to represent the correlation in image interpolation. However, linear models usually lack constraints of the representation coefficient. In this paper, we propose a low rank matrix recovery based image interpolation to reinforce the sparsity of representation coefficient implicitly. Since both the local and nonlocal correlation is pervasive in natural images,...
This paper proposes a new method of inter prediction based on low-rank matrix completion. By collection and rearrangement, image regions with high correlations can be used to generate a low-rank or approximately low-rank matrix. We view prediction values as the missing part in an incomplete low-rank matrix, and obtain the prediction by recovering the generated low-rank matrix. Taking advantage of...
This paper proposes a DCT analysis-based adaptive spatial deblocking algorithm to reduce the block discontinuity. We analyze the properties of the decoded one dimensional DCT coefficients. Based on this analysis, we utilize two adjacent vectors to classify the vector containing block boundary into three models. Then, for each model, we apply different filters according to the local properties. Simulation...
We propose a new no-reference image quality assessment model nPSNR (No-reference PSNR) for JPEG compressed images. The model performs in DCT domain and the DCT coefficient distribution is used. This method estimates the MSQE (mean-squared quantization error) of a decoded image with the distributions of AC coefficients and DC coefficients of the encoded image, for PSNR estimation in DCT domain without...
This paper introduces an adaptive postprocessing method in block-based discrete cosine transform (BDCT) coded images. We present a framework, which is a serial-concatenation of a 1-D simple deblocking filter and a 2-D directional filter. First, we classify the simple deblocking filter by an adaptive threshold depending on local statistical properties, and update block types appropriately by a simple...
Underwater video systems have assumed an increasingly important role in exploration and survey of the unknown deep ocean environments. This paper presents a real-time underwater video compression system that can be mounted on autonomous underwater vehicles (AUVs) for underwater survey applications. The hardware system is based on the digital media processor TMS320DM642 (DM642) of Texas Instruments,...
This paper introduces an adaptive postprocessing method in block-based discrete cosine transform (BDCT) coded images. We present a framework, which is a serial-concatenation of a 1-D simple deblocking filter and a 2-D directional filter. First, we classify the simple deblocking filter by an adaptive threshold depending on local statistical properties, and update block types appropriately by a simple...
To remove blocking artifacts, this paper proposes a novel adaptive postprocessing method in block-based discrete cosine transform (BDCT) coded images and presents a framework, which is a serial-concatenation of a simple deblocking filter and a constraint set based on the POCS algorithm. At first, we classify the simple deblocking filter by an adaptive threshold depending on local statistical properties,...
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