![]() Therefore, the first 6 bits are read from B (i.e., bits 001100, corresponding to v = 12). The number of bits g, which were used for encoding c 9, is then calculated as g = ⌈ log 2 ( 63 − 23 + 1 ) ⌉ = 6. As c 0 ≠ c 19, m = 9 is calculated with Equation ( 7). The decoder sets L = 0 and H = n − 1 = 19. In some domains, they are indispensable, such as compressing text, medical images, or high-quality sound, especially for editing purposes, to prevent accumulation of compression errors through repetitive compression and decompression.ĭecoding starts with reading the header, allocating the n = 20 memory units for sequence C, and initializing c 0 = 23 and c n − 1 = 63 (see Figure 7a). The lossless methods reconstruct the original data exactly. They enable users to specify exactly to what extent the errors in the reconstructed data are acceptable. Lossy methods cannot guarantee a distortion rate below the chosen limit at the level of an individual element (e.g., a pixel), which is why the near-lossless methods have been developed. Other techniques include, domain-specific triangulation or color reductions. Complete reconstruction is impossible because of this. Typically, transformations in the frequency domain are used to identify high-frequency components, which are quantized and eliminated permanently. The latter are domain-specific and consider the characteristics of humans’ senses for vision and hearing. The implementation of the proposed encoder is extremely simple and can be performed in less than 60 lines of programming code for the coder and 60 lines for the decoder, which is demonstrated in the given pseudocodes.ĭata compression algorithms can be classified as lossless, near-lossless, or lossy. ![]() JPEG LS turned out to be the most efficient, followed by JPEG 2000, while our approach using simplified interpolative coding was moderately better than PNG. Finally, our compression pipeline is compared against JPEG LS, JPEG 2000 in the lossless mode, and PNG using 24 standard grayscale benchmark images. Furthermore, the interpolative coding was moderately more efficient than the most frequently used arithmetic coding. It is determined that the JPEG LS predictor reduces the information entropy slightly better than the multi-functional approach. Its simplification is introduced in regard to the original approach. Then, interpolative coding, which has not been applied frequently for image compression, is explained briefly. A new multifunction prediction scheme is presented first. A new approach is proposed for lossless raster image compression employing interpolative coding.
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