Abstract: The discrete wavelet transform has recently emerged as a powerful technique for decomposing images into various multiresolution approximations. An image is decomposed into a sequence of orthogonal components, the first being an approximation of the original image at some 'base' resolution. By the addition of successive (orthogonal) 'error' images, approximations of higher resolution are obtained. Trellis coded quantization (TCQ) is known as an effective scheme for quantizing memoryless sources with low to moderate complexity. The TCQ approach to data compression has led to some of the most effective source codes found to date for memoryless sources. In this work, we investigate the use of entropy-constrained TCQ for encoding wavelet coefficients at different bit rates. The lowest-resolution sub-image is quantized using a 2-D discrete cosine transform encoder. For encoding the 512 $MUL 512, 8- bit, monochrome 'Lenna' image, a PSNR of 39.00 dB is obtained at an average bit rate of 0.89 bits/pixel.!17
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机译：摘要：离散小波变换最近成为一种强大的技术，可将图像分解为各种多分辨率近似值。图像被分解为一系列正交分量，第一个是原始图像在某个“基本”分辨率下的近似值。通过添加连续的（正交的）“误差”图像，可以获得更高的分辨率近似值。网格编码量化（TCQ）是一种有效的方案，用于以低到中等的复杂度来量化无记忆源。 TCQ的数据压缩方法导致了一些迄今为止最有效的无内存源代码。在这项工作中，我们研究了使用熵约束的TCQ在不同比特率下编码小波系数。使用2-D离散余弦变换编码器对最低分辨率的子图像进行量化。为了对512 $ MUL 512的8位单色'Lenna'图像进行编码，在0.89位/像素的平均比特率下可获得39.00 dB的PSNR！17
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