Wavelet-based L-infinite scalable coding

Abstract

A new wavelet-based embedded image coding technique is proposed. For any desired L ∞-distortion bound, the bit stream can be truncated at a corresponding bit-rate, so that the target upper bound on the absolute values of the pixels of the reconstruction error image is guaranteed. The algorithm outperforms embedded 2 L-oriented wavelet coders in terms of maximum absolute error.

On the Optimality of Embedded Deadzone Scalar-Quantizers for Wavelet-Based L-Infinite-Constrained Image Coding

“…It can be shown [4], [5] that for a lifting-based wavelet transform followed by uniform scalar quantization of the subbands, the MAXAD is given by (3) where is the total number of subbands, is the quantization bin size employed for subband , , and , are the contribution factors [4], [5] derived from the prediction and update coefficients of the lifting scheme. The notations here refer to the subbands raster scanning order [5], i.e., for subband HH, respectively for subband LL.…”

“…Various methods for -distortion constrained compression have been proposed in literature, some operating in the transform domain [1] or in the image domain [2], while others propose a hybrid bit-stream between the two [3]. We have recently proposed in [4] and [5] a wavelet-based -constrained scalable image-coding technique that generates a fully embedded -oriented bit-stream, while retaining the coding performance and scalability options of state-of-the-art wavelet-based codecs [6].…”

“…While state-of-the-art wavelet-based -oriented coders commonly employ embedded scalar quantizers having a double-sized deadzone [6], it is shown that this embedded quantizer instantiation should not be de facto employed in the case. Finally, the letter provides experimental results and the performance comparison of the coding scheme of [4] and [5], equipped with the optimal quantizers, versus an embedded codec belonging to the same wavelet-based family [6] and -oriented codecs that employ a different approach [2], [3].…”

Progressive lossless compression of medical images

“…The works of Avcibaş et al [3,4], Alecu et al [5,6,7] and Krivoulets [8,9] are remarkable exceptions, since they address the problem of generating embedded bit-streams that minimize the L∞-norm of the reconstruction error. Avcibaş et al proposed an approach that relies on a predictive-based method that successively refines the probability density function (pdf) used to estimate each pixel and by restricting the region of support of the pdf to fixed size intervals, which have to be predefined before encoding [3,4].…”

“…Avcibaş et al proposed an approach that relies on a predictive-based method that successively refines the probability density function (pdf) used to estimate each pixel and by restricting the region of support of the pdf to fixed size intervals, which have to be predefined before encoding [3,4]. Almost simultaneously, Alecu et al proposed a wavelet-based scheme that allows full L∞ scalability [5,6,7]. This algorithm was compared with JPEG2000 in terms of L∞ rate-distortion, showing better results [7].…”