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• W4313226001 abstract "This paper focuses on the ultimate limit theory of image compression. It proves that for an image source, there exists a coding method with shapes that can achieve the entropy rate under a certain condition where the shape-pixel ratio in the encoder/decoder is $O({1 over {log t}})$. Based on the new finding, an image coding framework with shapes is proposed and proved to be asymptotically optimal for stationary and ergodic processes. Moreover, the condition $O({1 over {log t}})$ of shape-pixel ratio in the encoder/decoder has been confirmed in the image database MNIST, which illustrates the soft compression with shape coding is a near-optimal scheme for lossless compression of images." @default.
• W4313226001 created "2023-01-06" @default.
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• W4313226001 date "2022-12-27" @default.
• W4313226001 modified "2023-10-14" @default.
• W4313226001 title "Why Shape Coding? Asymptotic Analysis of the Entropy Rate for Digital Images" @default.
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• W4313226001 doi "https://doi.org/10.3390/e25010048" @default.
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