l(2) Restoration of l(infinity)-Decoded Images Via Soft-Decision Estimation
The l(infinity)-constrained image coding is a technique to achieve substantially lower bit rate than strictly (mathematically) lossless image coding, while still imposing a tight error bound at each pixel. However, this technique becomes inferior in the l(2) distortion metric if the bit rate decreases further. In this paper, we propose a new soft decoding approach to reduce the l(2) distortion of l(infinity)-decoded images and retain the advantages of both minmax and least-square approximations. The soft decoding is performed in a framework of image restoration that exploits the tight error bounds afforded by the l(infinity)-constrained coding and employs a context modeler of quantization errors. Experimental results demonstrate that the l(infinity)-constrained hard decoded images can be restored to gain more than 2 dB in peak signal-to-noise ratio PSNR, while still retaining tight error bounds on every single pixel. The new soft decoding technique can even outperform JPEG 2000 (a state-of-the-art encoder-optimized image codec) for bit rates higher than 1 bpp, a critical rate region for applications of near-lossless image compression. All the coding gains are made without increasing the encoder complexity as the heavy computations to gain coding efficiency are delegated to the decoder.
context modeling image restoration markov random field mrf near-lossless image coding