$ell_{2}$ Restoration of $ell_{infty}$-Decoded Images Via Soft-Decision Estimation

摘要

The $ell_{infty}$-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 $ell_{2}$ distortion metric if the bit rate decreases further. In this paper, we propose a new soft decoding approach to reduce the $ell_{2}$ distortion of $ell_{infty}$-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 $ell_{infty}$-constrained coding and employs a context modeler of quantization errors. Experimental results demonstrate that the $ell_{infty}$-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.