Fixed-Length Golomb-Rice Coding By Quantization Level Estimation

Golomb-Rice coding is one of the popular variable-length codings which require quantization of input data to meet the target compression ratio. In order to obtain the optimal quantization level, a conventional iterative approach increases the quantization level one by one until the target ratio is achieved. This iterative approach makes it difficult to implement in hardware because the number of iterations cannot be estimated at hardware design time. This paper proposes a non-iterative algorithm for Golomb-Rice coding to estimate a near-optimal quantization level. To this end, the algorithm performs Golomb-Rice coding without any quantization of input data and then uses this coding result to estimate the codeword length with quantization. Based on the estimation, a near-optimal quantization level that meets the target length is selected. For the case when the selected level is not optimal, the algorithm performs additional Golomb-Rice codings with modified quantization levels which guarantee the codeword to meet the target length. Experimental results with twenty-four Kodak images show that the proposed coders practically cover all the optimal quantization levels.