A Human Vision System based Flash Picture Coding Method for Video Coding

Chen, Quqing; Nie, Zhengang; Chen, Zhibo; Gu, Xiaodong; Qiu, Guoping; Wang, C.

doi:10.1109/iscas.2007.378135

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But the offset model still can estimate the additive offset from the slight difference between P curr and P ref when coding the scene with fade-in from white or fade-out to white. In addition, the offset model is more suitable for the scenes with flashlight [12,13]. However, both of the DC and offset models suffer from low coding efficiency in the areas where the differences between the pixel values and the mean value of the frame are large.…”

Section: Overview Of Conventional Wp Modelsmentioning

confidence: 99%

Multiple weighted prediction models for video coding with brightness variations

Tsang¹,

Chan²,

Siu³

2012

IET Image Process.

View full text Add to dashboard Cite

Weighted prediction (WP) is a tool introduced in H.264 to improve motion-compensation performance for video sequences with brightness variations. Various WP models to estimate the parameter set have been discussed in the literature. However, no single WP model works well for all types of brightness variations. A single reference frame multiple WP models (SRefMWP) scheme is proposed to facilitate the use of multiple WP models in a single reference frame. The proposed scheme makes a new arrangement of the multiple frame buffers in multiple reference frame motion estimation. It enables different macroblocks in the same frame to use different WP models even when they are predicted from the same reference frame. Furthermore, a new reordering mechanism for SRefMWP is also proposed to guarantee that the list of the reference picture is in the best order for further decreasing the bit rate. To reduce the implementation cost, a reduction in the memory requirement is achieved via look-up tables (LUTs). Experimental results show that SRefMWP can achieve significant coding gain in scenes with different types of brightness variations. Furthermore, SRefMWP with LUTs can reduce the memory requirement by about 80% while keeping the same coding efficiency as SRefMWP.

show abstract

Section: Overview Of Conventional Wp Modelsmentioning

confidence: 99%