DWT based Scalable video coding with scalable motion coding Syed Jawwad Bukhari

Abstract The purpose of this project is to get understanding of DWT based scalable video compression. The purpose of this project is to get understanding of DWT based scalable video compression. For wider variety of bit rates we need wide range scalability and hence the motion information must also be presented in some scalable form to get such wide range scalability. For wider variety of bit rates we need wide range scalability and hence the motion information must also be presented in some scalable form to get such wide range scalability. This project is based on study of different DWT based video compression techniques. This project is based on study of different DWT based video compression techniques. Implementation of a Motion compensated Jpeg2000-like video coder. Implementation of a Motion compensated Jpeg2000-like video coder. Review of scalable motion paper [1] is also presented. Review of scalable motion paper [1] is also presented.

Introduction: Video Compression: Video Compression: Video compression methods use signal processing as well as video processing techniques for achieving maximum compression with in acceptable quality range. Trade off: Perceptual Quality vs. Compression ratio. Trade off: Perceptual Quality vs. Compression ratio.

Scalable Video Compression: Heterogeneous network users request for different type of quality for the same video Heterogeneous network users request for different type of quality for the same video E.g. E.g. a user with a lower bandwidth connection may require a low quality video a user with a lower bandwidth connection may require a low quality video a user watching the same video on a HDTV will require a video with much higher quality resulting in greater requirements for bandwidth. a user watching the same video on a HDTV will require a video with much higher quality resulting in greater requirements for bandwidth. A mobile user will require the same video in a lower resolution mode due to smaller screen size and memory requirements. A mobile user will require the same video in a lower resolution mode due to smaller screen size and memory requirements. => generic video coder to fulfill requirements for such a wide range of bit rates and qualities scalable video coding. scalable video coding.

Scalability Spatial scalability Spatial scalability Temporal scalability Temporal scalability SNR Scalability SNR Scalability Data partition. Data partition.  Motion information should also be scalable for wide range of bit rates to be covered.

DWT based scalable video coding DWT is widely used for scalable video coding. DWT is widely used for scalable video coding. various approaches involving DWT to achieve scalable video coding various approaches involving DWT to achieve scalable video coding 3D DWT, Temporal Filtering & Motion prediction 3D DWT, Temporal Filtering & Motion prediction In this project: In this project: Motion estimation on GOP (group of pictures) followed by 2D-DWT. (IMPLEMENTATION) Motion estimation on GOP (group of pictures) followed by 2D-DWT. (IMPLEMENTATION) EZW (REVIEW) EZW (REVIEW)

JPEG2K like MC video coder Image adapted from [5]

EZW (Embedded Zero-tree Wavelet) Shapiro presented the EZW algorithm in [4]. Shapiro presented the EZW algorithm in [4]

5 step bit-plane coding in EZW Step 1: Set an initial threshold such that will only the first coefficient is greater than the threshold and no other is greater than the threshold. Step 1: Set an initial threshold such that will only the first coefficient is greater than the threshold and no other is greater than the threshold. Step 2: Update the threshold to it’s half. Step 2: Update the threshold to it’s half.

5 step bit-plane coding in EZW Step 3: Significance pass. Scan insignificant values using baseline algorithm scan order as presented in the table above. Test each value if it greater than threshold then output the sign of value and set its quantized value to this threshold otherwise set the quantized value for this coefficient to zero. Step 3: Significance pass. Scan insignificant values using baseline algorithm scan order as presented in the table above. Test each value if it greater than threshold then output the sign of value and set its quantized value to this threshold otherwise set the quantized value for this coefficient to zero.

5 step bit-plane coding in EZW Step 4: Refinement pass. Scan significant values found with higher threshold values. For each significant value output a zero bit if it belongs to quantized value plus the threshold value interval otherwise output a one bit. Step 4: Refinement pass. Scan significant values found with higher threshold values. For each significant value output a zero bit if it belongs to quantized value plus the threshold value interval otherwise output a one bit. Step 5: Repeat step 2 to 4. Step 5: Repeat step 2 to 4.

Scalable motion coding [1] claims that at lower resolutions motion compensation is lesser sensitive [1] claims that at lower resolutions motion compensation is lesser sensitive it requires lesser accuracy. it requires lesser accuracy. According to [1] video is usually received at a lower resolution when transmission is over a lower bit rate channel. Its implication is that the motion vectors which were very accurate are almost useless as the smallest blocks vanish in lower resolutions. According to [1] video is usually received at a lower resolution when transmission is over a lower bit rate channel. Its implication is that the motion vectors which were very accurate are almost useless as the smallest blocks vanish in lower resolutions.

Implementations

Motion estimation algorithms were taken from [2], wavelet used and Huffman code for wavelet-based compression is also taken from [3]. Motion estimation algorithms were taken from [2], wavelet used and Huffman code for wavelet-based compression is also taken from [3]. S-HAT is used as a metric for measuring quality. Its implementation is based on the details given in Tobagi’s paper. S-HAT is used as a metric for measuring quality. Its implementation is based on the details given in Tobagi’s paper.

Parameters The parameters tested were The parameters tested were motion vector block size, motion vector block size, Search algorithm Search algorithm different setups of base and enhancements layers (spatial/ temporal/ data partition) different setups of base and enhancements layers (spatial/ temporal/ data partition) Study of both scalable video coding as well as scalable motion coding. Study of both scalable video coding as well as scalable motion coding.

Results Seq: Highway, B Sz: 8*8, Resolution: 256*256 Seq: Highway, B Sz: 8*8, Resolution: 256*256

Seq: Highway, B Sz: 8*8, Resolution: 256*256

Seq: Highway, B Sz: 8*8, Resolution: 128*128

S-HAT Seq: Highway Series 1 256*256, MB: 8*8, Series 2 128*128, MB: 4*4,

Compression Ratio Seq: Highway

References 1. “Accuracy-scalable motion coding for efficient scalable video compression”; Boisson, Edouard & Guillemot., MATLAB File Exchange. 3. Digital Image Processing using MATLAB, Gonzalez, “Embedded image coding using zero trees of wavelet coefficients.” IEEE Trans. on Signal Processing, 41(12): , “The transform and data compression handbook”