图像编码中容错性的应用研究
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摘要
无线通信中，信道容易发生突发差错，这将导致无线通信系统具有相对较高的误码率；而在有线通信中，由于网络带宽有限、拥塞等信道问题，将直接或间接地导致丢包、超时、比特传输错误、分组比特差错等。图像编码后在信道中传输时，必将由于上述问题而导致重建图像质量严重下降，为了保证重建图像的质量，这就需要研究图像编码算法的容错性。具有容错性的编解码系统中，如果发生误码，只要错误不是致命的，一般不需要将错误纠止过来。尤其在图像编码中，少量系数的错误不会对图像的视觉效果造成太大影响。容错编码的主要目的是增强系统的鲁棒性，使得图像的编解码系统不至于因为少量的误码而不能运行。
本文先简要介绍了一些经典和现代的图像编码方法，然后讨论和分析了一些现今的容错算法，如：基于小波变换的多描述图像编码、抗错锥形矢量量化图像压缩算法、打包的小波零树图像压缩编码算法、用于无线图像通信的基于遗传算法的多级矢量量化码书设计算法以及JPEG2000编码标准等。
然后，本文提出了基于SBC(子带位平面)的虚拟块容错算法。该算法分两步实现，第一步搭建SBC算法框架：首先将图像的小波分解系数按所属的子带分为位平面，称为子带位平面(即每个子带位平面均只局限在某一个子带内)，然后采用简单高效的率失真优化算法确定子带位平面的编码顺序，且这一顺序与图像无关，最后按照此顺序对系数比特进行上下文相关的处适应MQ算术编码。采用SBC算法后，对重建图像起关键作用的重要比特在全部的比特中只占很少的一部分，这也减小了其被“污染”的概率，而在全部比特中占大部分的是非重要比特，但它们对重建图像的质量影响很小，即使出错，也不会对重建图像的质量造成太大影响；第二步加入虚拟块容错算法。将图像划分成大小相等的若干块，对整幅图像(分块以前的图像)进行小波变换，小波变换之后，按照图像的划分将小波系数分成相应的块，对每个分块的系数用SBC算法编码；同时给每个子块的码流加上一个同步头，使得压缩以后各个子块能够独立的解码，即便有误码发生，也将会被限制在各个子块内部，而下一个子块在同步头的作用下又会正常解码，不会影响到相邻的子块的解码，这样就进一步提高了算法的容错能力。在上述研究基础上，我们设计并实现了基于SBC的虚拟块容错系统。
最后，本文对多幅标准测试图像进行了大量的实验，通过与PZW、ET-SPIHT、JPEG2000等性能较为不错的容错算法的比较，证明在不同压缩倍率下，本算法容错性整体上要更强(重建图像信噪比有1dB-2dB的提升)，也更为稳定；在不同的误码率下，本算法的容错性能也更强(重建图像信噪比有2dB-4dB的提升)，而且误码率越大，本算法的优势越明显。实验结果同时也表明本算法具备了一定的实用价值。
As we know, in wireless communication, wireless channels always produce outburst errors , so it will cause high BER in wireless communication system, and in wire communication, network always produce congestion or errors, so it will cause packet loss, time-out, bit transmission errors, bit block errors etc, when image code stream are being transmitted. After the coded image having been transmitted through the channel, based on the factors talked above, the quality of reconstructed image will be bound to drop dramatically. This demands us to study image error-resilient coding algorithm to ensure the quality of reconstructed image to be good. Once a error appears in coding systems of error-resilient characteristic, if only not a fatal error, it needn't to be corrected. Especially in image coding, a few coefficient errors won't influence vision effect much. Error-resilient's main purpose is to build up robust of the system so that the image coding/decoding system can be all right under a few errors.
In this paper, we introduce some classical coding methods and modern coding algorithm firstly, Secondly, we discuss and analyze some excellent error-resilient algorithm today, such as: multiple description image coding based on wavelet transforms, error-resilient pyramid vector quantization for image compression, robust wavelet zerotree image compression with fixed length packetization, multistage vector quantization codebook design for wireless image communication using genetic algorithms and JPEG2000 coding standard.
Then, we present the virtual block error-resilient algorithm based on SBC. The algorithm will be implemented in two steps, the first step is to set up the frame of SBC algorithm: Firstly, we divides the coefficients of integer-to-integer wavelet transform into bit planes within each subband which are named as subband bit-planes. For these subband bit-planes, the algorithm uses simple but effective rate-distortion optimization method to determine the coding order. According to the R-D optimized coding order, the coefficient bits are coded by adaptive MQ arithmetic coder. After we have adopted SBC algorithm, those significant bits which play a key part in reconstructing image are only a very little portion compared to all the bits, so the probability of significant bits being "polluted" is reduced. This means that a majority of all the bits are non-significant bits, but their influence on reconstructing image is very little, even though there are errors in them, the quality of reconstructed image will reduce l
ittle; the second step is to add virtual block error-resilient algorithm: firstly, we divide all the image into several equal
blocks, secondly, we apply wavelet transforms on the whole image (before the image is divided into several equal blocks), after wavelet transforms has been taken, we divide wavelet coefficient into correspond blocks according to image's being divided into several equal blocks, then we adopt SBC algorithm to code all the block's coefficient; At the same rime, we add a synchronization flag so that each block which has been compressed can be decoded independently. Even though errors occur in blocks, the errors will be restricted in blocks where errors occur, the next block will be decoded correctly in the affect of synchronization flag, so, the algorithm's ability of error-resilient will be enhanced further. Based on the research we have done before, we design and implement virtual block error-resilient system based on SBC algorithm.
In the end of the paper, we have done a great deal of experiments using many standard test images. Compare our algorism to those fine error-resilient algorithm such as PZW, ET-SPIHT\ JPEG2000 etc, we come to the conclusion that at different compression ratio, our error-resilient algorithm are stronger in the whole(the PSNR of reconstructed image increase ldB-2dB)and more steady; In different BER, our algorithm is also more robust than the others (the PSNR of reconstructed image increase 2dB-4dB), and higher the BER is, more excellent our algorithm w
本文先简要介绍了一些经典和现代的图像编码方法，然后讨论和分析了一些现今的容错算法，如：基于小波变换的多描述图像编码、抗错锥形矢量量化图像压缩算法、打包的小波零树图像压缩编码算法、用于无线图像通信的基于遗传算法的多级矢量量化码书设计算法以及JPEG2000编码标准等。
然后，本文提出了基于SBC(子带位平面)的虚拟块容错算法。该算法分两步实现，第一步搭建SBC算法框架：首先将图像的小波分解系数按所属的子带分为位平面，称为子带位平面(即每个子带位平面均只局限在某一个子带内)，然后采用简单高效的率失真优化算法确定子带位平面的编码顺序，且这一顺序与图像无关，最后按照此顺序对系数比特进行上下文相关的处适应MQ算术编码。采用SBC算法后，对重建图像起关键作用的重要比特在全部的比特中只占很少的一部分，这也减小了其被“污染”的概率，而在全部比特中占大部分的是非重要比特，但它们对重建图像的质量影响很小，即使出错，也不会对重建图像的质量造成太大影响；第二步加入虚拟块容错算法。将图像划分成大小相等的若干块，对整幅图像(分块以前的图像)进行小波变换，小波变换之后，按照图像的划分将小波系数分成相应的块，对每个分块的系数用SBC算法编码；同时给每个子块的码流加上一个同步头，使得压缩以后各个子块能够独立的解码，即便有误码发生，也将会被限制在各个子块内部，而下一个子块在同步头的作用下又会正常解码，不会影响到相邻的子块的解码，这样就进一步提高了算法的容错能力。在上述研究基础上，我们设计并实现了基于SBC的虚拟块容错系统。
最后，本文对多幅标准测试图像进行了大量的实验，通过与PZW、ET-SPIHT、JPEG2000等性能较为不错的容错算法的比较，证明在不同压缩倍率下，本算法容错性整体上要更强(重建图像信噪比有1dB-2dB的提升)，也更为稳定；在不同的误码率下，本算法的容错性能也更强(重建图像信噪比有2dB-4dB的提升)，而且误码率越大，本算法的优势越明显。实验结果同时也表明本算法具备了一定的实用价值。
As we know, in wireless communication, wireless channels always produce outburst errors , so it will cause high BER in wireless communication system, and in wire communication, network always produce congestion or errors, so it will cause packet loss, time-out, bit transmission errors, bit block errors etc, when image code stream are being transmitted. After the coded image having been transmitted through the channel, based on the factors talked above, the quality of reconstructed image will be bound to drop dramatically. This demands us to study image error-resilient coding algorithm to ensure the quality of reconstructed image to be good. Once a error appears in coding systems of error-resilient characteristic, if only not a fatal error, it needn't to be corrected. Especially in image coding, a few coefficient errors won't influence vision effect much. Error-resilient's main purpose is to build up robust of the system so that the image coding/decoding system can be all right under a few errors.
In this paper, we introduce some classical coding methods and modern coding algorithm firstly, Secondly, we discuss and analyze some excellent error-resilient algorithm today, such as: multiple description image coding based on wavelet transforms, error-resilient pyramid vector quantization for image compression, robust wavelet zerotree image compression with fixed length packetization, multistage vector quantization codebook design for wireless image communication using genetic algorithms and JPEG2000 coding standard.
Then, we present the virtual block error-resilient algorithm based on SBC. The algorithm will be implemented in two steps, the first step is to set up the frame of SBC algorithm: Firstly, we divides the coefficients of integer-to-integer wavelet transform into bit planes within each subband which are named as subband bit-planes. For these subband bit-planes, the algorithm uses simple but effective rate-distortion optimization method to determine the coding order. According to the R-D optimized coding order, the coefficient bits are coded by adaptive MQ arithmetic coder. After we have adopted SBC algorithm, those significant bits which play a key part in reconstructing image are only a very little portion compared to all the bits, so the probability of significant bits being "polluted" is reduced. This means that a majority of all the bits are non-significant bits, but their influence on reconstructing image is very little, even though there are errors in them, the quality of reconstructed image will reduce l
ittle; the second step is to add virtual block error-resilient algorithm: firstly, we divide all the image into several equal
blocks, secondly, we apply wavelet transforms on the whole image (before the image is divided into several equal blocks), after wavelet transforms has been taken, we divide wavelet coefficient into correspond blocks according to image's being divided into several equal blocks, then we adopt SBC algorithm to code all the block's coefficient; At the same rime, we add a synchronization flag so that each block which has been compressed can be decoded independently. Even though errors occur in blocks, the errors will be restricted in blocks where errors occur, the next block will be decoded correctly in the affect of synchronization flag, so, the algorithm's ability of error-resilient will be enhanced further. Based on the research we have done before, we design and implement virtual block error-resilient system based on SBC algorithm.
In the end of the paper, we have done a great deal of experiments using many standard test images. Compare our algorism to those fine error-resilient algorithm such as PZW, ET-SPIHT\ JPEG2000 etc, we come to the conclusion that at different compression ratio, our error-resilient algorithm are stronger in the whole(the PSNR of reconstructed image increase ldB-2dB)and more steady; In different BER, our algorithm is also more robust than the others (the PSNR of reconstructed image increase 2dB-4dB), and higher the BER is, more excellent our algorithm w
引文
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[10] Y Wang, S. Wenger etc. Error resilient video coding techniques. IEEE Signal Processing Magazine. Special issue on Multimedia Communications over Networks July 2000. 17(4): 61-82.
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