In our prior work, we presented an algorithm called largest magnitude coefficient selection (LMCS) for realizing the signal-to-noise-ratio (SNR) scaling an already encoded video object to an alternate (lower) rate (higher) distortion level. We showed that LMCS retains semantically important image features and successfully avoids the common artifacts of blur and ringing noise. However, it suffers from a significant coding inefficiency problem. In this paper, we first identify the mechanisms, which lead to the problem, and consequently present a novel technique called pivoting in order to alleviate it. The comparison of the resulting algorithm, LMCS-pivot, against the conventional SNR scaling techniques demonstrates its effectiveness according to both objective and subjective performance measures.

Ringing is an annoying artifact frequently encountered in low bit-rate transform and subband decomposition based compression of different media such as image, intra frame video and graphics. A mathematical morphology based post-processing algorithm is presented in this paper for image ringing artifact suppression. First, we use binary morphological operators to isolate the regions of an image where the ringing artifact is most prominent to the human visual system (HVS) while preserving genuine edges and other (high-frequency) fine details present in the image. Then, a gray-level morphological nonlinear smoothing filter is applied to the unmasked regions of the image under the filtering mask to eliminate ringing within this constraint region. To gauge the effectiveness of this approach, we propose an HVS compatible objective measure of the ringing artifact. Preliminary simulations indicate that the proposed method is capable of significantly reducing the ringing artifact on both subjective and objective basis.

The design and the implementation of an automated, design-rule-based, visual printed circuit board (PCB) inspection system are presented. The system employs mathematical-morphology-based image processing algorithms. This systems detects PCB defects related to the conducting structures on PCBs by checking a set of geometric design rules. For this purpose, an image segmentation algorithm and a defect detection algorithm are designed. The defect detection algorithm is capable of verifying the minimum conductor spacing, minimum conductor trace width, and the minimum land width requirements on digital binary PCB images. Also, an existing defect detection algorithm is modified for its implementation in the system.>