This papers proposes to compress state metrics in turbo decoder architectures to reduce the decoder area. Two techniques are proposed: the first is based on non-uniform quantization and the second on the Walsh-Hadamard transform followed by non-uniform quantization. The non-uniform quantization technique reduces state metric memory area of about 50% compared with architectures where state metric compression is not performed, at the expense of slightly increasing the error correcting performance floor. On the other hand, the Walsh-Hadamard transform based solution offers a good tradeoff between performance loss and memory complexity reduction, which reaches in the best case 20% of gain with respect to other approaches. Both solutions show lower power consumption than architectures previously proposed to compress state metrics.