It is assumed that high performance and quality, especially in the case of signal transmission, will lead to great acceptance and usability [...]

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Special Issue on Advance in Digital Signal, Image and Video Processing

Preserving the visual quality is a major constraint for any algorithm in image and video processing applications. AVC and HEVC are the extensively used video coding standards for various video processing applications in recent days. These coding standards use filters to preserve the visual quality of the processed video. To retain the quality of the reconstructed video, AVC uses an in-loop filter, called the deblocking filter, while HEVC uses two in-loop filters, the sampling adaptive offset filter and the deblocking filter. These filters are implemented in hardware by adopting various optimization techniques such as reduction of power utilization, reduction of algorithm complexity, and consuming lesser area. The quality of the reconstructed video should not be impacted by these optimization measures. For the HEVC/H.265 coding standard, a parallel edge deblocking filter architecture is designed, and the effectiveness of the parallel edge filter architecture is evaluated using various quantization values for various resolutions. The quality of the parallel edge filter architecture is on par with the HEVC reference model.

/pdf/quality-assessment-of-dual-parallel-edge-deblocking-filter-2kb7jgl3.pdf

Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265

Motion Estimation is used many video processing algorithms for video compression, moving object detection, 3D reconstruction, video segmentation, etc. Video standards use motion estimation in the encoder and decoder to compress the video. It becomes difficult to determine motion vectors when the video is prone to weather conditions like haze. So, there is a necessity to remove haze for video analysis. In this paper, hardware and software implementation of Motion Estimation (ME) and Haze removal techniques are reviewed. Architectures like low power and parallelism Motion Estimation techniques for Block Matching algorithms (BMA) in video coding standards and Deep Learning based methods Pixel-based Methods are studied and discussed. The atmospheric model for hazy images and videos is discussed. Techniques such as single-image dehazing are analyzed. Performance metrics carried out for video analysis are also mentioned. Based on the analysis and observation the advantages and drawbacks of these techniques are addressed. 

Prayline Rajabai Christopher

Papers

Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265

A survey on motion estimation and de-hazing algorithms and architectures