In the domain of digital video coding, new technologies and solutions are emerging in a fast pace, targeting the needs of the evolving multimedia landscape. One of the questions that arises is how to assess these different video coding technologies in terms of compression efficiency. In this paper, several compression schemes are compared by means of peak signal-to-noise ratio (PSNR) and just noticeable difference (JND). The codecs examined are XviD 0.9.1 (conform to the MPEG-4 Visual Simple Profile), DivX 5.1 (implementing the MPEG-4 Visual Advanced Simple Profile), Windows Media Video 9, MC-EZBC and H.264/AVC AHM 2.0 (version JM 6.1 of the reference software, extended with rate control). The latter plays a key role in this comparison because the H.264/AVC standard can be considered as the de facto benchmark in the field of digital video coding.
The obtained results show that H.264/AVC ARM 2.0 outperforms current proprietary and standards-based implementations in almost all cases. Another observation is that the choice of a particular quality metric can influence general statements about the relation between the different codecs
CMYK color images are used extensively in prepress applications. When compressing those color images one has to deal with four different color channels. Usually compression algorithms only take into account the spatial redundancy that is present in the image data. This approach does not yield an optimal data reduction since there also exists a high correlation between the different colors in natural images.
This paper shows that a significant gain in data reduction can be achieved by exploiting this color redundancy. Some popular transform coders, including DCT‐based JPEG and the SPIHT wavelet coder, were used for reducing the spatial redundancy. The performance of the algorithms was evaluated using a quality criterion based on human perception like the mean CIEL*a*b*ΔE error.
This paper shows that an n x 1 integer vector can be exactly recovered from its Hadamard transform coefficients, even when 0.5 n log(2)(n) of the (less significant) bits of these coefficients are removed. The paper introduces a fast "lossless" dequantization algorithm for this purpose. To investigate the usefulness of the procedure in data compression, the paper investigates an embedded block image coding technique called the "LHAD" based on the algorithm. The results show that lossless compression ratios close to the state of the art can be achieved, but that techniques such as CALIC and S+P still perform better.
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