Sabir Muhammad F, Sheikh Hamid Rahim, Heath Robert W, Bovik Alan C
Laboratory for Image and Video Engineering, Department of Electrical and Computer Engineering, The University of Texas at Austin, TX 78712-1084, USA.
IEEE Trans Image Process. 2006 Jun;15(6):1349-64. doi: 10.1109/tip.2006.871118.
The need for efficient joint source-channel coding (JSCC) is growing as new multimedia services are introduced in commercial wireless communication systems. An important component of practical JSCC schemes is a distortion model that can predict the quality of compressed digital multimedia such as images and videos. The usual approach in the JSCC literature for quantifying the distortion due to quantization and channel errors is to estimate it for each image using the statistics of the image for a given signal-to-noise ratio (SNR). This is not an efficient approach in the design of real-time systems because of the computational complexity. A more useful and practical approach would be to design JSCC techniques that minimize average distortion for a large set of images based on some distortion model rather than carrying out per-image optimizations. However, models for estimating average distortion due to quantization and channel bit errors in a combined fashion for a large set of images are not available for practical image or video coding standards employing entropy coding and differential coding. This paper presents a statistical model for estimating the distortion introduced in progressive JPEG compressed images due to quantization and channel bit errors in a joint manner. Statistical modeling of important compression techniques such as Huffman coding, differential pulse-coding modulation, and run-length coding are included in the model. Examples show that the distortion in terms of peak signal-to-noise ratio (PSNR) can be predicted within a 2-dB maximum error over a variety of compression ratios and bit-error rates. To illustrate the utility of the proposed model, we present an unequal power allocation scheme as a simple application of our model. Results show that it gives a PSNR gain of around 6.5 dB at low SNRs, as compared to equal power allocation.
随着商业无线通信系统中引入新的多媒体服务，对高效联合信源信道编码（JSCC）的需求日益增长。实用的JSCC方案的一个重要组成部分是一个失真模型，它可以预测诸如图像和视频等压缩数字多媒体的质量。JSCC文献中用于量化量化和信道错误引起的失真的常用方法是针对给定信噪比（SNR），利用图像的统计信息对每个图像进行估计。由于计算复杂度，这在实时系统设计中不是一种有效的方法。一种更有用且实用的方法是基于某种失真模型设计能使大量图像的平均失真最小化的JSCC技术，而不是进行逐图像优化。然而，对于采用熵编码和差分编码的实际图像或视频编码标准，目前还没有能以组合方式估计大量图像由于量化和信道误码引起的平均失真的模型。本文提出了一种统计模型，用于联合估计渐进式JPEG压缩图像中由于量化和信道误码引入的失真。该模型包含了诸如霍夫曼编码、差分脉冲编码调制和游程编码等重要压缩技术的统计建模。示例表明，在各种压缩率和误码率下，以峰值信噪比（PSNR）衡量的失真可以在最大误差2dB内进行预测。为了说明所提出模型的实用性，我们提出了一种不等功率分配方案作为该模型的一个简单应用。结果表明，与等功率分配相比，在低信噪比下它能提供约6.5dB的PSNR增益。
