Abstract:

Scalable high efficiency video coding (SHVC) standard is expected to play a more important role in the heterogeneous landscape of broadcasting, multimedia, networks, and various services applications as it is specified as a layered coding technique in the advanced television systems committee 3.0. However, its block-based structure of temporal and spatial prediction makes it sensitive to information loss and error propagation due to transmission errors. In this context, we propose an improved SHVC with a joint layer prediction (JLP) solution which adaptively combines the decoded information from the base and the enhancement layers to create an additional reference for the SHVC enhancement encoder. To optimize the quality of the joint prediction, the minimum mean square error estimation is executed in computing a combination factor which gives weights to each contribution of the decoded information from the layers. In addition, the proposed JLP is integrated into the SHVC decoder to work as an error concealment solution to mitigate the error propagation happening inevitably in practical video transmission. Experiments have shown that the proposed SHVC framework significantly outperforms its relevant benchmarks, notably by up to 14.8% in bitrate reduction with respect to the standard SHVC codec. The proposed SHVC error concealment strategy also greatly improves the concealed picture quality as well as reducing the problem of error propagation when compared to conventional error concealment approaches.

Abstract:

The emerging scalable High Efficiency Video Coding (SHVC) video coding standard provides an efficient solution for transmission of video over heterogeneous and time dynamic networks, terminals, and usage environments. The encoding complexity and the error sensitivity associated with the efficient HEVC coding tools adopted in SHVC make this scalable codec less attractive to some emerging applications such as video surveillance, visual sensor network, and remote space transmission where these requirements are critical. To address the requirements of these application scenarios including scalability, this paper proposes a novel HEVC-based framework offering quality scalability on top of an HEVC compliant base layer while appropriately combining the predictive and distributed coding paradigms. To achieve the best enhancement layer compression efficiency, two novel coding tools are proposed, notably a machine learning-based side information creation mechanism and an adaptive correlation modeling process. The experimental results reveal that the rate-distortion performance of the proposed distributed scalable video coding-HEVC solution outperforms the relevant alternative coding solutions, notably by up to 52.9% and 23.7% BD-rate gains regarding the HEVC-Simulcast and SHVC standard solutions, respectively, for an equivalent prediction configuration, while achieving a lower encoding complexity.

Abstract

Printed Circuit Boards (PCBs) are critical components in modern electronics, which require stringent quality control to ensure proper functionality. However, the detection of defects in small-scale PCBs images poses significant challenges as a result of the low resolution of the captured images, leading to potential confusion between defects and noise. To overcome these challenges, this paper proposes a novel framework, named ESRPCB (edge-guided super-resolution for PCBs defect detection), which combines edge-guided super-resolution with ensemble learning to enhance PCBs defect detection. The framework leverages the edge information to guide the EDSR (Enhanced Deep Super-Resolution) model with a novel ResCat (Residual Concatenation) structure, enabling it to reconstruct high-resolution images from small PCBs inputs. By incorporating edge features, the super-resolution process preserves critical structural details, ensuring that tiny defects remain distinguishable in the enhanced image. Following this, a multi-modal defect detection model employs ensemble learning to analyze the super-resolved image, improving the accuracy of defect identification. Experimental results demonstrate that ESRPCB achieves superior performance compared to State-of-the-Art (SOTA) methods, achieving an average Peak Signal to Noise Ratio (PSNR) of 30.54 , surpassing EDSR by . In defect detection, ESRPCB achieves a mAP50(mean average precision at an Intersection over Union threshold of 0.50) of 0.965, surpassing EDSR (0.905) and traditional super-resolution models by over 5%. Furthermore, the ensemble-based detection approach further enhances performance, achieving a mAP50 of 0.977. These results highlight the effectiveness of ESRPCB in enhancing both image quality and defect detection accuracy, particularly in challenging low-resolution scenarios.