A low complex scalable spatial adjacency ACC-DCT based video compression method

In this paper, we propose a low complex Scalable ACC-DCT based video compression approach which tends to hard exploit the pertinent temporal redundancy in the video frames to improve compression efficiency with less processing complexity. Generally, video signal has high temporal redundancies due to the high correlation between successive frames. Actually, this redundancy has not been exposed enough by current video compression techniques. Our model consists on 3D to 2D transformation of the video frames that allows exploring the temporal redundancy of the video using 2D transforms and avoiding the computationally demanding motion compensation step. This transformation turns the spatial temporal correlation of the video into high spatial correlation. Indeed, this technique transforms each group of pictures (GOP) to one picture (Accordion Representation) eventually with high spatial correlation. This model is also incorporated with up/down sampling method (SVC) which is based on a combination of the forward and backward type discrete cosine transform (DCT) coefficients. As this kernel has various symmetries for efficient computation, a fast algorithm of DCT-based Scalability concept is also proposed. For further improvement of the scalable performance, an adaptive filtering method is introduced, which applies different weighting parameters to DCT coefficients. Thus, the decorrelation of the resulting pictures by the DCT makes efficient energy compaction, and therefore produces a high video compression ratio. Many experimental tests had been conducted to prove the method efficiency especially in high bit rate and with slow motion video. The proposed method seems to be well suitable for video surveillance applications and for embedded video compression systems.