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Inverse Error Diffusion For Low Bit Rate Coding Using Table Look-Up And Vector Quantization

This paper considers a new algorithm for inverse error diffusion and data compression of binary error diffused images using table look-up and vector quantization (VQ). This method integrates the processes of inverse error diffusion and compression. The encoding process is based on a table look-up which transforms error diffused images to a set of vector quantized codewords. The decoding process requires table look-up and image restoration. Several image restoration techniques have been applied in order to find the most appropriate one. By using the proposed scheme the reconstructed gray-scale image is stored in a compressed form. Besides that, error diffused image can be significantly compressed in the gray-scale domain. The proposed scheme outperforms similar existing techniques in the terms of signal-to-noise ratio and subjective quality, for low bit rates (0.25bpp to 0.5bpp).

inverse error diffusion; vector quantization; table look-up; images; low bit rate

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