Download presentation

Published byJake Whitehead Modified over 4 years ago

1
**Low-Complexity Transform and Quantization in H.264/AVC**

Henrique S. Malvar, Fellow, IEEE, Antti Hallapuro, Marta Karczewicz, and Louis Kerofsky, Member, IEEE

2
**Content DCT and Integer transform**

Brief recall of the H.264 encode and decode structure Transform in H.264 DCT and Integer transform Low-Complexity integer transform(author proposed) Quantization in H.264

3
**H.264 encode & decode Three Parts: Prediction, Transform, Quantization**

Input block Prediction Transform Quantization Entropy Coding Transmit Prediction: Generate block prediction by Motion Estimation. Transform: Convert the difference between the prediction and true value into coefficients by integer transform. Quantization: Quantize the coefficients.

4
**DCT & Integer Transform**

DCT(Discrete Cosine Transform) Commonly used in block transform coding of image and video, e.g. JPEG and MPEG. Definition for 8x8 block: Convert image from spatial domain to frequency domain

5
**DCT & Integer Transform**

In H.264, 4x4 block transform is adopted Problem: Coefficients are irrational numbers. In digital computer, when you do an inverse transform after forward transform of an input, It may not get the same input back.

6
**DCT & Integer Transform**

Solution: Integer Transform An integer approximation of DCT. Original H.264 design: {a=13, b=7, c=17} Problem: increase of dynamic range. If max(X(i,j))=A, then max(Y(u,v))= A x (13x4)^2 = 2704 x A. Log2(2704) = 11.4, So it needs 12 more bits to encode Y(u,v) than X(i,j)

7
**Low-Complexity Integer Transform**

Choose {a=1, b=2, c=1} Rows are orthogonal to each other. The dynamic range gain is log2(6^2) = 5.17 Although the norm of each row is different, it can be easily compensated in quantization part. No noticeable performance penalty while reducing the dynamic range gain and simplicity.

8
**Low-Complexity Integer Transform**

Inverse transform We could just use the transpose of H. However, in order to minimize the dynamic range gain, we scale the rows that has element 2 in H’ by ½. So it becomes, Dynamic range gain = log2(4^2) = 4 bits. Also, the factor ½ can be realized by right shift 1 bit, so no multiplication needed.

9
**Low-Complexity Integer Transform**

Inverse transform Forward transform

10
QUANTIZATION It is the step that introduces signal loss for better compression. Encoder quantization is given by where controls the quantization width near the origin. The decoder produces reverse quantization by

11
Rules of Quantization There must be as low complexity as possible since the H.264 uses predictive coding which means that the error will tend to drift over the entire set for each prediction. Memory requirements are very high for 32- bit operations hence the arithmetic must be as close to 16-bit as possible. There must be no undue stress on the hardware yet keeping the prediction drift error free.

12
**The disadvantage of the quantizing equation is that it divides by an integer .**

In the H.264 format the quantization is of the form The inverse quantization is given by The values A(Q) and B(Q) are obtained from the quantization tables.

13
**In the previous equation**

And Q varies from 0 to Qmax. Hence 0 is the finest and Qmax is the coarsest quantization. Care must be taken during shifting the bits right since repeated division means tending towards negative infinity and not 0. In the original H.264 design, L=N=20.

14
**The values A(Q) and B(Q) must satisfy the form**

The values A(Q) and B(Q) must satisfy the form where G is the squared norm of the rows of H. The values of L & N are chosen on a compromise. Larger values reduce approximation error in the above equation and smaller values reduce dynamic range.

15
**16 Bit Arithmetic and Quantization Tables**

The complexity of quantization formulae are reduced considerably by reducing them to 16 bits. However, this reduction must be traded off with no reduction in PSNR. This is done by effectively reducing values of B(Q), L & N. B(Q) effectively doubles for an increase of 6 in Q making it a linear relationship between PSNR and Q. This makes it easier to design quantization and reconstruction tables.

16
**Quantization and Reconstruction Tables**

The H.264 hence uses the modified quantization and reconstruction formulae Where The mod operator makes the quantization factor periodic making it easy to define a large range of parameters without increasing memory requirements

17
**The matrices shown denote values of A(Q) and B(Q) such that the matrices maximise dynamic range.**

These ensure that results always fall within a 16 bit result.

Similar presentations

OK

Image Processing Architecture, © 2001-2004 Oleh TretiakPage 1Lecture 5 ECEC 453 Image Processing Architecture Lecture 5, 1/22/2004 Rate-Distortion Theory,

Image Processing Architecture, © 2001-2004 Oleh TretiakPage 1Lecture 5 ECEC 453 Image Processing Architecture Lecture 5, 1/22/2004 Rate-Distortion Theory,

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google

Download ppt on p block elements class 11 Ppt on bank lending rate Ppt on essay writing skills Ppt on classical economics wikipedia Ppt on mahatma gandhi life in hindi Ppt on cartesian product sql Ppt on alternative sources of energy Ppt on hydrogen fuel cell vehicles Export pdf to ppt online convert Ppt on global warming in hindi language