Lossless Image Coding Using Conditional Entropy Constrained Vector Quantization
Kishwar R. Naushahi,
Mohammad A.U. Khan
M. Sikander H. Khiyal
Lossless coding guaranties that the decompressed image is absolutely identical to the image before compression. This is an important requirement for some application domains, e.g. medial imaging, where not only high quality is in demand, but unaltered archiving is a legal requirement. In this study an entropy constrained vector quantization is proposed for lossless compression of image. The method consists of first quantizing the input image using conditional entropy constrained vector quantizer and then coding the residual images using entropy coder. Experimental results show that the new method outperforms standard entropy constrained vector quantization to achieve lossless compression while also requiring lower encoding complexity and memory requirements.
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