A novel adaptive resolution upconversion algorithm that is robust to compression artifacts is proposed. This method is based on classification of local image patterns using both structure information and activity measure to explicitly distinguish pixels into content or coding artifacts. The structure information is represented by adaptive dynamic-range coding and the activity measure is the combination of local entropy and dynamic range. For each pattern class, the weighting coefficients of upscaling are optimized by a least-mean-square (LMS) training technique, which trains on the combination of the original images and the compressed downsampled versions of the original images. Experimental results show that our proposed upconversion approach outperforms other classification-based upconversion and artifact reduction techniques in concatenation.