Systems and methods for reduced bit-depth processing in video-related data with frequency weighting matrices

Abstract

Embodiments of the present invention comprise systems and methods for processing of data related to video wherein reduced bit depth intermediate calculations are enabled.

I claim: 
     
       1. A method for dequantization and inverse transformation, said method comprising:
 (a) receiving a matrix of quantized coefficient levels;   (b) receiving at least one quantization parameter (QP);   (c) determining a reconstructed transform coefficient (RTC) matrix wherein each value in said quantized coefficient level matrix is scaled by a value in a scaling matrix which is dependent on QP % P, where P is a constant value;   (d) computing scaled reconstructed samples (SRS) by performing an inverse transformation on said RTC matrix values; and   (e) computing reconstructed samples, by normalizing the SRS values.   
     
     
       2. A method as described in  claim 1  wherein P=6. 
     
     
       3. A method as described in  claim 1  wherein said scaling matrix is a 4×4 matrix. 
     
     
       4. A method as described in  claim 1  wherein said scaling matrix is an 8×8 matrix. 
     
     
       5. A method as described in  claim 1  wherein said at least one quantization parameter (QP) comprises a chroma quantization parameter. 
     
     
       6. A method as described in  claim 1  wherein said at least one quantization parameter (QP) comprises a luma quantization parameter. 
     
     
       7. A method as described in  claim 1  wherein said at least one quantization parameter (QP) comprises a chroma quantization parameter for each chroma channel. 
     
     
       8. A method as described in  claim 1  wherein said at least one quantization parameter (QP) comprises a chroma quantization parameter for each chroma channel and a luma quantization parameter. 
     
     
       9. A method for dequantization and inverse transformation, said method comprising:
 (a) receiving a matrix of quantized coefficient levels (QCL matrix);   (b) receiving a quantization parameter (QP);   (c) calculating a scaling matrix using a weighting matrix scaled by a dequantization matrix selected using QP % P;   (d) determining a reconstructed transform coefficient (RTC) matrix wherein said QCL matrix is scaled by said scaling matrix;   (e) computing scaled reconstructed samples (SRS) by performing an inverse transformation on said RTC matrix values; and   (f) computing reconstructed samples, by normalizing the SRS values with a constant shift operation.   
     
     
       10. A method as described in  claim 9  further comprising shifting said RTC matrix values by a value dependent on QP/P before said computing scaled reconstructed samples. 
     
     
       11. A method for dequantization and inverse transformation, said method comprising:
 (a) fixing a limited set of scaling matrices, wherein each of said scaling matrices in said limited set of scaling matrices is dependent on an associated quantization parameter QP and an associated constant parameter P according to the relation QP % P;   (b) receiving a quantized coefficient level (QCL) matrix;   (c) determining a reconstructed transform coefficient (RTC) matrix wherein each value in said quantized coefficient level matrix is scaled by a value in a scaling matrix that is selected from said limited set of scaling matrices;   (d) computing scaled reconstructed samples (SRS) by performing an inverse transformation on said RTC matrix values; and   (e) computing reconstructed samples, by normalizing the SRS values.   
     
     
       12. An apparatus for dequantization and inverse transformation, said apparatus comprising:
 (a) a QCL receiver for receiving a matrix of quantized coefficient levels (QCLs);   (b) a QP receiver for receiving at least one quantisation parameter (QP);   (c) a processor, wherein said processor is capable of determining a reconstructed transform coefficient (RTC) matrix wherein each value in said quantized coefficient level matrix is scaled by a value in a scaling matrix which is dependent on QP % P, where P is a constant value;   (d) said processor comprising a further capability of computing scaled reconstructed samples (SRS) by performing an inverse transformation on said RTC matrix values; and   (e) said processor comprising the capability of computing reconstructed samples, by normalizing said SRS values.   
     
     
       13. A computer-readable medium encoded with computer executable instructions for dequantization and inverse transformation, said instructions comprising:
 (a) receiving a matrix of quantized coefficient levels;   (b) receiving at least one quantization parameter (QP);   (c) determining a reconstructed transform coefficient (RTC) matrix wherein each value in said quantized coefficient level matrix is scaled by a value in a scaling matrix which is dependent on QP % P, where P is a constant value;   (d) computing scaled reconstructed samples (SRS) by performing an inverse transformation on said RTC matrix values; and   (e) computing reconstructed samples, by normalizing the SRS values.   
     
     
       14. A video decoder comprising a processor, the processor configured to generate a differential image X of an image sample from a quantized value L, the differential image X representing a difference between a current image data and a prior image data, the decoder being configured to:
 receive the quantized value L;   perform an inverse transformation of the quantized value L to derive a value Y; and   perform combined dequantization and normalization of the inverse transformed value Y to generate a differential image X of an image sample, wherein the combined dequantization and normalization comprises:
 inputting a quantization parameter QP, 
 deriving the differential image X of an image sample,
 wherein the differential image X of an image sample is derived by using a mantissa portion Bm(QP) being a function of the quantization parameter QP and an exponential portion Be(QP) being a function of the quantization parameter QP according to a function X=Y*Bm(QP)*2 Be(QP) *2 −N , 
 wherein N is an integer, the multiplication by 2 −N  represents a final normalization shift and the mantissa portion Bm(QP) and the exponential portion Be(QP) satisfy recursion relations
 Bm(QP+6)=Bm(QP), and 
 Be(QP+6)=Be(QP)+1; and 
 
 
   generate the current image data by combining the differential image X and the prior image data.