System and method for enhancing videos from drift-free scalable bitstream

• US 20030223642A1
• Filed: 05/29/2002
• Published: 12/04/2003
• Est. Priority Date: 05/29/2002
• Status: Active Grant

• Current Assignee: Funai Electric Co., Ltd. (Funai Electric Holdings Company Limited)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Chen, Yingwei, Ye, Jong Chul
• Granted Patent: US 6,917,713 B2
• Time in Patent Office: Days
• US Class Current: 382/233
• CPC Class Codes: H04N 19/126 Details of normalisation or weighting functions, e.g. normalisation matrices or variable uniform quantisers
• CPC Class Codes: H04N 19/34 Scalability techniques involving progressive bit-plane based encoding of the enhancement layer, e.g. fine granular scalability [FGS]
• CPC Class Codes: H04N 19/61 in combination with predictive coding
• CPC Class Codes: H04N 19/895 in combination with error concealment

Abstract

A scalable decoder system having a decoder reconstruction system, wherein the decoder reconstruction system comprises: an estimation algorithm ${\tilde{x}}_{i,n}^e=a_{i,n}+E_Z\left\{d_{i,n}\left(\rho ;{\tilde{x}}_{i,n-1}^e+Z\right)\right\}$

for estimating a lost discrete cosine transform (DCT) coefficient, which incorporates the quantization noise of previous enhancement layer data into the expectation, wherein the estimation algorithm includes a density distribution dependent on at least one unknown parameter {circumflex over (α)}_l; and a parameter estimation algorithm for estimating {circumflex over (α)}_l according to the equation: ${\hat{\alpha }}_l=-\frac{\log \left(1-{\hat{P}}_l\right)}{b_l},$

wherein b_l denotes each of a set of quantizer decision levels l, and {circumflex over (P)}_l is the sum of a set of a normalized frequency of occurrences of data quantized to each quantizer decision level 1,2, . . . , l.

Claims

1. A scalable decoder system having a decoder reconstruction system, wherein the decoder reconstruction system comprises:

an estimation algorithm for estimating a lost discrete cosine transform (DCT) coefficient, wherein the estimation algorithm includes a density distribution dependent on at least one unknown parameter {circumflex over (α

)}_l; and

a parameter estimation algorithm for estimating {circumflex over (α

)}_l according to the equation;

${\widehat{\mathrm{\alpha <br><br>}}}_l=-\frac{\log <br><br>\left(1-{\hat{P}}_l\right)}{b_l},$

wherein b_l denotes each of a set of quantizer decision levels l, and {circumflex over (P)}_l is the sum of a set of a normalized frequency of occurrences of data quantized to each quantizer decision level 1,2, . . . , l.

2. The scalable decoder of claim 1, wherein {circumflex over (P)}_l is chosen to be approximately equal to one.

3. The scalable decoder of claim 1, wherein the estimation algorithm is given by:

4. The scalable decoder of claim 3, wherein ρ

equals approximately 1 for low frequency DCT coefficients, and less than 1 for high frequency DCT coefficients.

5. A parameter estimation and optimal reconstruction (PEOR) method for use in a scalable decoder, the method comprising:

determining the set of quantization occurrences;

estimating a parameter a, wherein a is estimated according to the equation

${\widehat{\mathrm{\alpha <br><br>}}}_l=-\frac{\log <br><br>\left(1-{\hat{P}}_l\right)}{b_l},$

wherein b_l denotes each of a set of quantizer decision levels l, and {circumflex over (P)}_l is the sum of a set of a normalized frequency of occurrences of data quantized to each quantizer decision level 1,2, . . . ,l;

generating a preliminary reconstruction point according to the equation

${\tilde{x}}_{i,n}^e=a_{i,n}+d_{i,n}<br><br>\left(\mathrm{\rho <br><br>};<br><br>{\tilde{x}}_{i,n-1}^e\right);<br><br>$

and

smoothing the preliminary reconstruction point to generate an optimal reconstruction point.

6. The method of claim 5, wherein the set of quantization occurrences are determined by counting DCT coefficients with a quantizer index of j and then dividing the result by the total number of DCT coefficients.

7. The method of claim 5, wherein the step of smoothing the preliminary reconstruction point is accomplished according to the equation:

8. A parameter estimation and optimal reconstruction (PEOR) system for use in a scalable decoder, comprising:

a reconstruction system for determining an optimal reconstruction according to the equation;

${\tilde{x}}_{i,n}^e=a_{i,n}+E_z<br><br>\left\{d_{i,n}<br><br>\left(\mathrm{\rho <br><br>};<br><br>{\tilde{x}}_{i,n-1}^e+Z\right)\right\},$

wherein Z is a quantization noise, E_z is an expectation with respect to quantization noise Z,

$d_{i,n}<br><br>\left(\mathrm{\rho <br><br>};<br><br>{\tilde{x}}_{i,n-1}^e+Z\right)$

denotes a reconstruction offset,

${\tilde{x}}_{i,n-1}^e$

is an expectation of a discrete cosine transform (DCT) coefficient of a previous frame, ρ

equals a predetermined value between −

1 and 1, and a_i,n is a beginning of a quantization interval.

9. The PEOR system of claim 8, wherein the parameter wherein α

is estimated according to the equation;