Adaptive Spatial and Transform Domain FGS Coding

In inter-picture coding, block-based frequency transform is usually carried out on the predicted errors for each interblock to remove the spatial correlation among them. However, it can not always do well since the predicted errors in some inter-blocks have marginal or diagonal correlation. A good solution is to omit transform operations for the predicted errors of those inter-blocks with low correlation before quantization operation. The same phenomenon also can be observed in fine grain scalability (FGS) layer coding. In this paper, an adaptive prediction error coding method in spatial and frequency domain with lower complexity is considered for FGS layer coding. Transform operation is only needed when there are non-zero reconstructed coefficients in spatially co-located block in base layer. The experimental results show that compared with FGS coding in JSVM, higher coding efficiency can be achieved with lower computational complexity at decoder since inverse transform is no longer needed for those predicted errors coded in spatial domain at encoder.