L2 RESTORATION OF L∞-DECODED IMAGES WITH CONTEXT MODELING

The L∞-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 (colloquially referred to as near-lossless image coding). However, this technique becomes inferior in the $L_{2}$ distortion metric if the bit rate decreases further. We propose a new soft decoding approach to reduce the $L_{2}$ distortion of $L_{\infty}$-coded images, benefiting from the advantages of both minmax and mean square approximations. This is made possible by context modeling of quantization distortions and by exploiting the $L_{\infty}$ bound inherent to near-lossless coding in a framework of image restoration. In addition, the proposed soft decoding approach offers an asymmetric high-fidelity image compression solution: the encoder is of low complexity with heavy computations of gaining coding efficiency performed by the decoder. Experimental results demonstrate that the new soft decoding approach can improve the PSNR of $L_{\infty}$-decoded images by more than 1 dB, and it can even outperform JPEG 2000 (a state-of-the-art encoder-optimized image codec) for bit rates higher than 1.17 bpp, while achieving much tighter $L_{\infty}$ error bound.