Low-rate image coding with finite-state vector quantization

Abstract

Direct space-domain image coding with Vector Quantization (VQ) has been very effective in the range 0.6 to 1.5 bpp. To achieve quality at lower rates, it is necessary to exploit spatial redundancy over a larger region of pixels than is possible with memoryless VQ. One way to do this is to incorporate memory into a VQ-based coder. A finite-state vector quantizer (FSVQ) employs a state variable which summarizes the past to select one of a family of codebooks to encode each vector. As a result, an FSVQ can achieve the same performance as memoryless VQ at lower rates. In this paper we extend the FSVQ technique to image coding, introducing a novel formulation of the state and state-transition rule using a perceptually-based image classifier. A new distortion measure is proposed for image VQ, which appears to be perceptually more effective than the usual MSE-criterion and leads to a modified LBG design algorithm to obtain codebooks which result in improved edge integrity. At 0.375 bpp, the resulting FSVQ coder achieves performance comparable to earlier memoryless VQs at 0.7 bpp.