Pre-charge phase data compression

• US 10,057,577 B2
• Filed: 06/11/2015
• Issued: 08/21/2018
• Est. Priority Date: 06/11/2015
• Status: Active Grant

• Current Assignee: Sony Corporation (Sony Group Corp.)
• Original Assignee: Sony Corporation (Sony Group Corp.)
• Inventors: Cheong, Hye-Yeon, Tabatabai, Ali
• Primary Examiner(s): Motsinger, Sean
• Application Number:
  US14/736,597
• Publication Number:
  US 20160366410A1
• Time in Patent Office: 1,167 Days
• CPC Class Codes: H04N 19/124 Quantisation
• CPC Class Codes: H04N 19/13 Adaptive entropy coding, e.g. adaptive variable length coding [AVLC] or context adaptive binary arithmetic coding [CABAC]
• CPC Class Codes: H04N 19/136 Incoming video signal characteristics or properties
• CPC Class Codes: H04N 19/146 Data rate or code amount at the encoder output
• CPC Class Codes: H04N 19/176 the region being a block, e.g. a macroblock
• CPC Class Codes: H04N 19/184 the unit being bits, e.g. of the compressed video stream
• CPC Class Codes: H04N 19/192 the adaptation method, adaptation tool or adaptation type being iterative or recursive
• CPC Class Codes: H04N 19/593 involving spatial prediction techniques

Abstract

Implementations generally relate to pre-charge phase data compression. In some implementations, a method includes computing prediction values for image data, where the image data is pre-charge phase data. The method also includes computing residual data based on the prediction values. The method also includes quantizing the residual data. The method also includes entropy encoding the quantized residual data. The method also includes refining an inverse quantized residual data based on one or more of the residual data and a number of left-over bit-budget after entropy encoding.

Claims

1. An apparatus comprising:

one or more processors; and

logic encoded in one or more tangible media for execution by the one or more processors and when executed operable to perform operations including;

computing prediction values for image data, wherein the image data is pre-charge phase data;

computing residual data based on the prediction values;

determining a average prediction value based on the prediction values, wherein the average prediction value is computed for each block;

computing the residual data based on the average prediction value;

quantizing the residual data, wherein the quantizing of the residual data is based on a quantization value;

entropy encoding the quantized residual data; and

refining an inverse quantized residual data based on the residual data and a number of left-over bit-budget after entropy encoding.

2. The apparatus of claim 1, wherein one or more of the processors are implemented in an image sensor.

3. The apparatus of claim 1, wherein the logic when executed is further operable to perform operations comprising:

performing middle point reconstruction and adds the residual data to the prediction values.

4. The apparatus of claim 1, wherein the quantization value is selected from one or more predetermined quantization values.

5. The apparatus of claim 1, wherein the entropy encoding is based on Golomb coding.

6. Software encoded in one or more non-transitory computer-readable media for execution by one or more processors and when executed operable to perform operations comprising:

computing prediction values for image data, wherein the image data is pre-charge phase data;

computing residual data based on the prediction values;

determining a average prediction value based on the prediction values, wherein the average prediction value is computed for each block;

computing the residual data based on the average prediction value;

quantizing the residual data, wherein the quantizing of the residual data is based on a quantization value;

entropy encoding the quantized residual data; and

refining an inverse quantized residual data based on the residual data and a number of left-over bit-budget after entropy encoding.

7. The software of claim 6, wherein the logic when executed is further operable to perform operations comprising:

performing middle point reconstruction and adds the residual data to the prediction values.

8. The software of claim 6, wherein the quantizing of the residual data is based on a quantization value, and wherein the quantization value is selected from one or more predetermined quantization values.

9. The software of claim 6, wherein the entropy encoding is based on Golomb coding.

10. A computer-implemented method comprising:

computing prediction values for image data, wherein the image data is pre-charge phase data;

computing residual data based on the prediction values;

determining a average prediction value based on the prediction values, wherein the average prediction value is computed for each block;

computing the residual data based on the average prediction value;

quantizing the residual data, wherein the quantizing of the residual data is based on a quantization value;

entropy encoding the quantized residual data; and

refining an inverse quantized residual data based on the residual data and a number of left-over bit-budget after entropy encoding.

11. The method of claim 10, wherein the logic when executed is further operable to perform operations comprising:

performing middle point reconstruction and adds the residual data to the prediction values.

12. The method of claim 10, wherein the quantization value is selected from one or more predetermined quantization values.