Information loss recovery techniques are important for transmitting images over error-prone channels at the decoder. A novel error recovery scheme for JPEG2000 image is presented in this paper, which adopts different ...Information loss recovery techniques are important for transmitting images over error-prone channels at the decoder. A novel error recovery scheme for JPEG2000 image is presented in this paper, which adopts different techniques for the lowest frequency coefficients and high frequency coefficients in the wavelet domain. The low-frequency recovery algorithm was implemented by adopting the watermarking technique and the packet structure of JPEG2000. The low-frequency eoefficients taken as the hidden data were extracted from the compressed bit stream, and then were embedded back into the bit stream itself prior to transmission. The embedded data were used to recover the information loss. High-frequency reconstruction was performed in bitplane base. The damaged bitplanes were recovered according to the correlation in the wavelet subband structure and by using the algorithm based on the horizontal and vertical edge detection. Experiments verified the effectiveness of these algorithms.展开更多
<span style="font-family:Verdana;">In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images </span><span style="font-...<span style="font-family:Verdana;">In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images </span><span style="font-family:Verdana;">ha</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ve</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> become a real problem. Image compression </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> the first approach to address this problem, it nevertheless suffers from its inability to adapt to the dynamics of limited environments, consisting mainly of mobile equipment and wireless networks. In this work, we propose a stochastic model to gradually estimate an image upon </span><span style="font-family:Verdana;">information</span><span style="font-family:Verdana;"> on its pixels that are transmitted progressively. We consider this transmission as a </span><span style="font-family:Verdana;">dynamical</span><span style="font-family:Verdana;"> process, where the sender </span><span style="font-family:Verdana;">push</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">es</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> the data in decreasing significance order. In order to adapt to network conditions and performances, instead of truncating the pixels, we suggest a new method called Fast Reconstruction Method by Kalman Filtering (FRM-KF) consisting </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> recursive inference of the not yet received layers belonging to a sequence of bitplanes. After empirical analysis, we estimate </span><span style="font-family:Verdana;">parameters</span><span style="font-family:Verdana;"> of our model which is a linear discrete Kalman Filter. We assume the initial law of information to be the uniform distribution on the set [0, 255] corresponding to the range of gray levels. The performances of FRM-KF method ha</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ve </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">been evaluated in terms of the ratios in the quality of data image/size sent and in the quality of image/time required for treatment. </span><span style="font-family:Verdana;">A high</span><span style="font-family:Verdana;"> quality was reached faster with relatively small data (less than 10% of image data is needed to obtain up to the sixth-quality image). The time for treatment also decreases faster with </span><span style="font-family:Verdana;">number</span><span style="font-family:Verdana;"> of received layers. However, we found that the time of image treatment might be large starting from </span><span style="font-family:Verdana;">a image</span><span style="font-family:Verdana;"> resolution of 1024 * 1024. Hence, we recommend </span><span style="font-family:Verdana;">FRM-KF</span><span style="font-family:Verdana;"> method for resolutions less or equal to 512 * 512. A statistical comparative analysis reveals that FRM-KF is competitive and suitable to be implemented </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> limited </span><span style="font-family:Verdana;">resource</span><span style="font-family:Verdana;"> environments.</span></span></span></span>展开更多
As an elegant generalization of wavelet transform, wavelet packet （WP） provides an effective representation tool for adaptive waveform analysis. Recent work shows that image-coding methods based on WP decomposition ...As an elegant generalization of wavelet transform, wavelet packet （WP） provides an effective representation tool for adaptive waveform analysis. Recent work shows that image-coding methods based on WP decomposition can achieve significant gain over those based on a usual wavelet transform. However, most of the work adopts a tree-structured quantization scheme, which is a successful technique for wavelet image coding, but not appropriate for WP subbands. This paper presents an image-coding algorithm based on a rate-distortion optimized wavelet packet decomposition and on an intraband block-partitioning scheme. By encoding each WP subband separately with the block-partitioning algorithm and the JPEG2000 context modeling, the proposed algorithm naturally avoids the difficulty in defining parent-offspring relationships for the WP coefficients, which has to be faced when adopting the tree-structured quanUzation scheme. The experimental results show that the proposed algorithm significantly outperforms SPIHT and JPEG2000 schemes and also surpasses state-of-the-art WP image coding algorithms, in terms of both PSNR and visual quality.展开更多
文摘Information loss recovery techniques are important for transmitting images over error-prone channels at the decoder. A novel error recovery scheme for JPEG2000 image is presented in this paper, which adopts different techniques for the lowest frequency coefficients and high frequency coefficients in the wavelet domain. The low-frequency recovery algorithm was implemented by adopting the watermarking technique and the packet structure of JPEG2000. The low-frequency eoefficients taken as the hidden data were extracted from the compressed bit stream, and then were embedded back into the bit stream itself prior to transmission. The embedded data were used to recover the information loss. High-frequency reconstruction was performed in bitplane base. The damaged bitplanes were recovered according to the correlation in the wavelet subband structure and by using the algorithm based on the horizontal and vertical edge detection. Experiments verified the effectiveness of these algorithms.
文摘<span style="font-family:Verdana;">In a context marked by the proliferation of smartphones and multimedia applications, the processing and transmission of images </span><span style="font-family:Verdana;">ha</span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ve</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> become a real problem. Image compression </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> the first approach to address this problem, it nevertheless suffers from its inability to adapt to the dynamics of limited environments, consisting mainly of mobile equipment and wireless networks. In this work, we propose a stochastic model to gradually estimate an image upon </span><span style="font-family:Verdana;">information</span><span style="font-family:Verdana;"> on its pixels that are transmitted progressively. We consider this transmission as a </span><span style="font-family:Verdana;">dynamical</span><span style="font-family:Verdana;"> process, where the sender </span><span style="font-family:Verdana;">push</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">es</span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;"> the data in decreasing significance order. In order to adapt to network conditions and performances, instead of truncating the pixels, we suggest a new method called Fast Reconstruction Method by Kalman Filtering (FRM-KF) consisting </span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">of</span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"> recursive inference of the not yet received layers belonging to a sequence of bitplanes. After empirical analysis, we estimate </span><span style="font-family:Verdana;">parameters</span><span style="font-family:Verdana;"> of our model which is a linear discrete Kalman Filter. We assume the initial law of information to be the uniform distribution on the set [0, 255] corresponding to the range of gray levels. The performances of FRM-KF method ha</span></span></span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">ve </span></span></span><span><span><span style="font-family:;" "=""><span style="font-family:Verdana;">been evaluated in terms of the ratios in the quality of data image/size sent and in the quality of image/time required for treatment. </span><span style="font-family:Verdana;">A high</span><span style="font-family:Verdana;"> quality was reached faster with relatively small data (less than 10% of image data is needed to obtain up to the sixth-quality image). The time for treatment also decreases faster with </span><span style="font-family:Verdana;">number</span><span style="font-family:Verdana;"> of received layers. However, we found that the time of image treatment might be large starting from </span><span style="font-family:Verdana;">a image</span><span style="font-family:Verdana;"> resolution of 1024 * 1024. Hence, we recommend </span><span style="font-family:Verdana;">FRM-KF</span><span style="font-family:Verdana;"> method for resolutions less or equal to 512 * 512. A statistical comparative analysis reveals that FRM-KF is competitive and suitable to be implemented </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> limited </span><span style="font-family:Verdana;">resource</span><span style="font-family:Verdana;"> environments.</span></span></span></span>
基金the Major State Basic Research Development Program(973 Program)(Grant No.2004CB318005)
文摘As an elegant generalization of wavelet transform, wavelet packet （WP） provides an effective representation tool for adaptive waveform analysis. Recent work shows that image-coding methods based on WP decomposition can achieve significant gain over those based on a usual wavelet transform. However, most of the work adopts a tree-structured quantization scheme, which is a successful technique for wavelet image coding, but not appropriate for WP subbands. This paper presents an image-coding algorithm based on a rate-distortion optimized wavelet packet decomposition and on an intraband block-partitioning scheme. By encoding each WP subband separately with the block-partitioning algorithm and the JPEG2000 context modeling, the proposed algorithm naturally avoids the difficulty in defining parent-offspring relationships for the WP coefficients, which has to be faced when adopting the tree-structured quanUzation scheme. The experimental results show that the proposed algorithm significantly outperforms SPIHT and JPEG2000 schemes and also surpasses state-of-the-art WP image coding algorithms, in terms of both PSNR and visual quality.
