**Weighted Recurrent Quality Enhancement**, or **WRQE**, is a recurrent quality enhancement network for video compression that takes both compressed frames and the bit stream as inputs. In the recurrent cell of WRQE, the memory and update signal are weighted by quality features to reasonably leverage multi-frame information for enhancement.

**Inception-v3 Module** is an image block used in the [Inception-v3](https://paperswithcode.com/method/inception-v3) architecture. This architecture is used on the coarsest (8 × 8) grids to promote high dimensional representations.

Inception-v3 Module

Rethinking the Inception Architecture for Computer Vision

WRQE

Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement

**FractalNet** is a type of convolutional neural network that eschews [residual connections](https://paperswithcode.com/method/residual-connection) in favour of a "fractal" design. They involve repeated application of a simple expansion rule to generate deep networks whose structural layouts are precisely truncated fractals. These networks contain interacting subpaths of different lengths, but do not include any pass-through or residual connections; every internal signal is transformed by a filter and nonlinearity before being seen by subsequent layers.

Source	Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com