Performance analysis of efficient video transmission using EvalSVC, EvalVid-NT, EvalVid

Dawood, M. Sheik; Benazer, S. Sakena; Karthick, R.; Ganesh, R.; Mary, Shruti

doi:10.1016/j.matpr.2021.02.287

Cited by 4 publications

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Semantic guidance incremental network for efficiency video super-resolution

He,

Xia,

Qiao

et al. 2024

Vis Comput

View full text Add to dashboard Cite

In video streaming, bandwidth constraints significantly affect client-side video quality. Addressing this, deep neural networks offer a promising avenue for implementing video super-resolution (VSR) at the user end, leveraging advancements in modern hardware, including mobile devices. The principal challenge in VSR is the computational intensity involved in processing temporal/spatial video data. Conventional methods, uniformly processing entire scenes, often result in inefficient resource allocation. This is evident in the over-processing of simpler regions and insufficient attention to complex regions, leading to edge artifacts in merged regions. Our innovative approach employs semantic segmentation and spatial frequency-based categorization to divide each video frame into regions of varying complexity: simple, medium, and complex. These are then processed through an efficient incremental model, optimizing computational resources. A key innovation is the sparse temporal/spatial feature transformation layer, which mitigates edge artifacts and ensures seamless integration of regional features, enhancing the naturalness of the super-resolution outcome. Experimental results demonstrate that our method significantly boosts VSR efficiency while maintaining effectiveness. This marks a notable advancement in streaming video technology, optimizing video quality with reduced computational demands. This approach, featuring semantic segmentation, spatial frequency analysis, and an incremental network structure, represents a substantial improvement over traditional VSR methodologies, addressing the core challenges of efficiency and quality in high-resolution video streaming.

show abstract