IMPROVING FRAME INTERPOLATION WITH SPATIAL MOTION SMOOTHING FOR PIXEL DOMAIN DISTRIBUTED VIDEO CODING

By João Ascenso, Catarina Brites, Fernando Manuel Bernardo Pereira. 2005

432 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Yun Raymond Fu
3. Ming-Hsuan Yang
4. Chen Change Loy
5. Lei Zhang
6. Xiaogang Wang
7. Dacheng Tao
8. Trevor Darrell

Citations in the corpus

• Deep multi-scale video prediction beyond mean square error (2015, methodology): “Beside unsupervised learning, a video predictive system may find applications in robotics (Kosaka & Kak, 1992), video compression (Ascenso et al., 2005) and inpainting (Flynn et al., 2015) to name a few.”
• The DISCOVER codec: Architecture, Techniques and Evaluation (2007, methodology): “The following techniques [8][11] are used to obtain high quality side information.”
• TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution (2018, methodology): “Generic: The proposed TDAN is a generic temporal alignment framework and can be easily utilized to replace flow-based motion compensation for several other tasks, such as video denoising [19], video deblocking [18], video deblur-ring [8], video frame interpolation [1], and even video prediction [30].”