At lowbit-rates, the conventional imagecoding standards, e.g., JPEG and JPEG 2000, do not have good compression performance due to the insufficiency of codingbits. A common solution to this problem is downsampling ...
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ISBN:
(纸本)9781509013456
At lowbit-rates, the conventional imagecoding standards, e.g., JPEG and JPEG 2000, do not have good compression performance due to the insufficiency of codingbits. A common solution to this problem is downsampling before encoding and reconstruction after decoding. Inspired by the wavelet domain downsampling-based compression scheme, we establish an enhanced lowbit-ratescoding framework by making the following improvements. Firstly, a regression priors-based coding artifacts reduction (RCAR) method is incorporated to preprocess the decoded low-resolution (LR) image;secondly, given the decoded low frequency wavelet coefficients, we propose to estimate its corresponding high frequency wavelet coefficients by using the joint optimized regressors (JOR) model to recover more information lost in downsampling phase;finally, the effective group-based sparse representation (GSR) model, which exploits both the nonlocal self-similarity and local sparsity properties, is utilized to perform soft decoding on the result of wavelet reconstruction. Experimental results suggest that the proposed framework outperforms JPEG 2000 at low to medium bit-rates in terms of both quantitative and visual comparisons.
At lowbit-rates, the conventional imagecoding standards, e.g., JPEG and JPEG 2000, do not have good compression performance due to the insufficiency of codingbits.A common solution to this problem is downsampling b...
详细信息
At lowbit-rates, the conventional imagecoding standards, e.g., JPEG and JPEG 2000, do not have good compression performance due to the insufficiency of codingbits.A common solution to this problem is downsampling before encoding and reconstruction after decoding. Inspired by the wavelet domain downsampling-based compression scheme, we establish an enhanced lowbit-ratescoding framework by making the following improvements. Firstly, a regression priors-based coding artifacts reduction(RCAR) method is incorporated to preprocess the decoded low-resolution(LR) image;secondly,given the decoded low frequency wavelet coefficients, we propose to estimate its corresponding high frequency wavelet coefficients by using the joint optimized regressors(JOR) model to recover more information lost in downsampling phase;finally, the effective group-based sparse representation(GSR) model, which exploits both the nonlocal self-similarity and local sparsity properties, is utilized to perform soft decoding on the result of wavelet reconstruction. Experimental results suggest that the proposed framework outperforms JPEG 2000 at low to medium bit-rates in terms of both quantitative and visual comparisons.
In this paper, a new compressive sampling-based imagecoding scheme is developed to achieve competitive coding efficiency at lower encoder computational complexity, while supporting error resilience. This technique is...
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In this paper, a new compressive sampling-based imagecoding scheme is developed to achieve competitive coding efficiency at lower encoder computational complexity, while supporting error resilience. This technique is particularly suitable for visual communication with resource-deficient devices. At the encoder, compact image representation is produced, which is a polyphase down-sampled version of the input image;but the conventional low-pass filter prior to down-sampling is replaced by a local random binary convolution kernel. The pixels of the resulting down-sampled pre-filtered image are local random measurements and placed in the original spatial configuration. The advantages of the local random measurements are two folds: 1) preserve high-frequency image features that are otherwise discarded by low-pass filtering and 2) remain a conventional image and can therefore be coded by any standardized codec to remove the statistical redundancy of larger scales. Moreover, measurements generated by different kernels can be considered as the multiple descriptions of the original image and therefore the proposed scheme has the advantage of multiple description coding. At the decoder, a unified sparsity-based soft-decoding technique is developed to recover the original image from received measurements in a framework of compressive sensing. Experimental results demonstrate that the proposed scheme is competitive compared with existing methods, with a unique strength of recovering fine details and sharp edges at lowbit-rates.
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