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Codec from Canada outperforms H.264 video with wavelets

Posted: 08 Dec 2006     Print Version  Bookmark and Share

Keywords:wavelet  video codec  H.264  algorithms 

Exploding demand for video services, such as HDTV broadcasting, IPTV, mobile TV, and D-cinema, creates a sustained need or more efficient video coding in order to reduce storage and transmission bandwidth. All the existing video coding standards are based on the hybrid coding scheme of motion compensation(MC) and discrete cosine transform (DCT). The latest video coding standard, H.264/MPEG-AVC, is a well-refined and optimised version of this hybrid scheme. Its compression efficiency is up to 50% higher than that of the widely used MPEG-2standard. But is the hybrid MC/DCT scheme able to offer even higher compression efficiency to fulfil the requirements of thefuture video services? A number of experts don't think that the hybrid scheme still has significant potential to exploit, considering the fact that this scheme has been well refined and optimised during the last two decades. Therefore, they turn their attention to other schemes.

Wavelet-based video coding has recently received much attention and emerged as a powerful competitor against the traditional hybrid coding scheme. Many experimental results have shown that wavelet-based video coding is able to provide higher compression efficiency than the traditional hybrid coding. In addition, wavelet-based video coding has another advantage.

Once a video is encoded at a given resolution and quality, video with various lower resolutions and qualities can be easily decoded using portions of the bit streams. This feature, called scalability, enables delivery of video over heterogeneous networks and to serve clients with various display and processing capabilities.

In this article we will describe the structure and algorithms of a wavelet-based video codec called CRC-WVC. Then we will present the compression efficiency of the codec and explain the reasons why it can perform better than H.264.

Structure of a wavelet- based video codec
In wavelet-based video coding, a video sequence is divided into groups of pictures (GOP) and encoded GOP by GOP. The motion within each GOP is estimated. A 3-D wavelet transform is applied to the GOP to remove its temporal and spatial redundancies; the resulting wavelet coefficients are then encoded using an entropy coding technique.

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Figure 1: Block diagram of the wavelet-based video codec, CRC-WVC.

Figure 1 show the structure of our wavelet-based video codec, CRC-WVC, where MCTF stands for motion-compensated temporal filtering. In fact, the MCTF is a one-dimensional (1-D) multi-level wavelet transform along motion trajectories. It removes the temporal redundancy within each GOP.

The wavelet transform itself is a filtering and sub-sampling recursive process. The GOP to be transformed is first filtered using a low-pass and a highpass wavelet filter, respectively, along motion trajectories. The outputs of the filters are then sub-sampled by a factor of two, resulting in pictures of low-pass and high-pass wavelet coefficients, as shown in Figure 2. Then, the filtering and sub-sampling process is repeatedly applied to the resulting low-pass pictures to produce a multi-level transform. In CRC-WVC, the 5/3 wavelet filters are employed for the MCTF. A new signal extension method and sub-sampling rule are used to improve the performance of the MCTF.

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Figure 2: The first MCTF level.

The motion required by the MCTF is estimated with a hierarchical variable size block-matching algorithm. The resulting motion vectors are predicted with an efficient algorithm and the prediction errors are encoded with an arithmetic coding. After the MCTF, every resulting picture undergoes a 2-D multilevel wavelet transform to remove the spatial redundancy existing in the picture. This 2-D transform is also performed through 1-D filtering and subsampling.

The picture is filtered, first along each column, using the low-pass and high-pass 9/7 wavelet filters. The outputs of these filters are sub-sampled by discarding every other row, resulting in low-pass coefficients L and high-pass coefficients H. Then the picture consisting of the L and H coefficients is filtered and sub-sampled along each row using the same filters and sub-sampling rule. This results in four different groups of coefficients, LL1, LH1, HL1, and HH1 coefficients. Each of the four groups of coefficients is arranged to form a sub-picture, as shown in Figure 3. The filtering and sub-sampling process is repeatedly applied to the LL1 subpicture to produce a multi-level 2-D wavelet transform. After the temporal and spatial wavelet transform, the signal energy of the GOP is concentrated to the low-pass coefficients at the highest temporal and highest spatial level.

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Figure 3: Spatial wavelet transform, (a) one level, (b) two levels.


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