Sparsity-driven reconstruction of ℓ∞-decoded images

In this paper, we propose a sparsity-driven restoration technique to improve the coding performance of the ℓ_∞-decoded images. This is achieved by incorporating a ℓ₁ minimization term into a ℓ₂ optimization framework, where the weighting vectors balancing the relative contribution of each term are appropriately determined. The ℓ_∞ constraints inherent to ℓ_∞-constrained predictive coding are also included to narrow the solution space, leading to more accurate estimation. Experimental results show that our proposed scheme significantly improves the ℓ₂ performance of the ℓ_∞-decoded images, while still preserving a tight error bound on every single pixel. In addition, when comparing with the existing scheme of restoring the ℓ_∞-decoded images, the PSNR gain can be up to 1 dB.