Today's digital signal processing (DSP) applications use computationally complex and/or adaptive algorithms and have stringent requirements in terms of speed, size, cost, power consumption, and throughput. Efficient hardware implementation techniques should be employed to meet the requirements of these applications. Run-Time Reconfiguration (RTR) is a promising technique for reducing the hardware required for implementing DSP systems as well as improving the performance, speed and power consumption of these systems. In this survey, we explain different issues in run-time reconfigurable systems and list the implemented systems which support run-time reconfiguration. We also describe different applications of run-time reconfiguration and discuss the improvements achieved by applying run-time reconfiguration.

In this paper, we propose a new encoding algorithm for matching pursuit image coding. We show that coding performance is improved when correlations between atom positions and atom coefficients are both used in encoding. We find the optimum tradeoff between efficient atom position coding and efficient atom coefficient coding and optimize the encoder parameters. Our proposed algorithm outperforms the existing coding algorithms designed for matching pursuit image coding. Additionally, we show that our algorithm results in better rate distortion performance than JPEG 2000 at low bit rates.

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In this paper, an adaptive in-loop quantization technique is proposed for quantizing inner product coefficients in matching pursuit. For each matching pursuit (MP) stage a different quantizer is used based on the probability distribution of MP coefficients. The quantizers are optimized for a given rate budget constraint. Additionally, our proposed adaptive quantization scheme finds the optimal quantizers for each stage based on the already encoded inner product coefficients. Experimental results show that our proposed adaptive quantization scheme outperforms existing quantization methods used in matching pursuit image coding.