IP Library › Granted Patent US 12,347,148
Granted Patent B2
US 12,347,148 · App. 17/881,432 · Granted Jul 1, 2025

Image processing method and related device

Inventors: Jing Wang (Beijing, CN); Ze Cui (Beijing, CN); Bo Bai (Beijing, CN)
Assignee: Huawei Technologies Co., Ltd.
G06T9/00G06T3/4007G06V10/48
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,347,148
App. No.
17/881,432
Granted
Jul 1, 2025
Kind
B2
Abstract

An image processing method and apparatus are provided. The method includes: obtaining an image; performing feature extraction on the image to obtain at least one first feature map, wherein the at least one first feature map includes N first feature values, and N is a positive integer; obtaining a target compression bit rate which corresponds to M target gain values, each target gain value corresponds to one first feature value, and M is a positive integer less than or equal to N; respectively processing corresponding first feature values based on the M target gain values to obtain M second feature values; and performing quantization and entropy encoding on at least one processed first feature map to obtain encoded data, wherein the at least one processed first feature map includes the M second feature values. According to the application, compression bit rate control is implemented in a same compression model.

Claims (41)

1. An image processing method applied to an image processing device comprising one or more processors, the method comprising:

obtaining a first image;

performing feature extraction on the first image to obtain at least one first feature map, wherein the at least one first feature map comprises N first feature values, and N is a positive integer;

obtaining a target compression bit rate;

determining, based on a target mapping relationship, M target gain values corresponding to the target compression bit rate, wherein the target mapping relationship indicates an association relationship between a compression bit rate and M target gain values, each target gain value of the M target gain values corresponds to a first feature value of the N first feature values, and M is a positive integer less than or equal to N;

respectively processing corresponding first feature values based on the M target gain values to obtain M second feature values; and

performing quantization and entropy encoding on at least one processed first feature map to obtain encoded data, wherein the at least one processed first feature map comprises the M second feature values;

wherein the target mapping relationship comprises a target function mapping relationship, and based on an input of the target function mapping relationship comprising the target compression bit rate, an output of the target function mapping relationship comprises the M target gain values.

2. The method according to claim 1 , wherein information entropy of quantized data obtained by quantizing the at least one processed first feature map meets a preset condition, and the preset condition is related to the target compression bit rate.

3. The method according to claim 2 , wherein the preset condition comprises at least:

a larger target compression bit rate indicates larger information entropy of the quantized data.

4. The method according to claim 1 , wherein a difference between a compression bit rate corresponding to the encoded data and the target compression bit rate falls within a preset range.

5. The method according to claim 1 , wherein the M second feature values are obtained by separately performing a multiplication operation on the M target gain values and the corresponding first feature values.

6. The method according to claim 1 , wherein the at least one first feature map comprises a first target feature map, the first target feature map comprises P first feature values, all of the P first feature values correspond to a same target gain value of the M target gain values, and P is a positive integer less than or equal to M.

7. The method according to claim 1 , wherein the target compression bit rate is greater than a first compression bit rate and less than a second compression bit rate, the first compression bit rate corresponds to M first gain values, the second compression bit rate corresponds to M second gain values, and the M target gain values are obtained by performing an interpolation operation on the M first gain values and the M second gain values.

8. The method according to claim 1 , wherein the first image comprises a target object, and M first feature values are feature values that are in the at least one first feature map and that correspond to the target object.

9. The method according to claim 1 , wherein each target gain value of the M target gain values corresponds to one reverse gain value, the reverse gain value is used to process a feature value obtained in a decoding process of the encoded data, and a product of each target gain value of the M target gain values and the corresponding reverse gain value falls within a preset range.

10. An image processing method applied to an image processing device comprising one or more processors, the method comprising:

obtaining encoded data;

performing entropy decoding on the encoded data to obtain at least one second feature map, wherein the at least one second feature map comprises N third feature values, and N is a positive integer;

obtaining a target compression bit rate;

determining, based on a target mapping relationship, M target reverse gain values corresponding to the target compression bit rate, wherein the target mapping relationship indicates an association relationship between a compression bit rate and a reverse gain vector, wherein each target reverse gain value of the M target reverse gain values corresponds to a third feature value of the N third feature values, and M is a positive integer less than or equal to N;

respectively processing corresponding third feature values based on the M target reverse gain values, to obtain M fourth feature values; and

performing image reconstruction on at least one processed second feature map to obtain a second image, wherein the at least one processed second feature map comprises the M fourth feature values,

wherein the target mapping relationship comprises a target function mapping relationship, and based on an input of the target function mapping relationship comprising the target compression bit rate, an output of the target function mapping relationship comprises the M target reverse gain values.

11. The method according to claim 10 , wherein the M fourth feature values are obtained by separately performing a multiplication operation on the M target reverse gain values and the corresponding third feature values.

12. The method according to claim 10 , wherein the at least one second feature map comprises a second target feature map, the second target feature map comprises P third feature values, all of the P third feature values correspond to a same target reverse gain value of the M target reverse gain values, and P is a positive integer less than or equal to M.

13. The method according to claim 10 , wherein the second image comprises a target object, and M third feature values are feature values that are in the at least one second feature map and that correspond to the target object.

14. The method according to claim 10 , wherein the target compression bit rate is greater than a first compression bit rate and less than a second compression bit rate, the first compression bit rate corresponds to M first reverse gain values, the second compression bit rate corresponds to M second reverse gain values, and the M target reverse gain values are obtained by performing an interpolation operation on the M first reverse gain values and the M second reverse gain values.

15. A device, comprising:

one or more processors;

a non-transitory computer-readable storage medium coupled to the one or more processors and storing instructions that, upon being executed by the one or more processors, cause the device to perform operations comprising:

obtaining a first image;

performing feature extraction on the first image to obtain at least one first feature map, wherein the at least one first feature map comprises N first feature values, and N is a positive integer;

obtaining a target compression bit rate;

determining, based on a target mapping relationship, M target gain values corresponding to the target compression bit rate, wherein the target mapping relationship indicates an association relationship between a compression bit rate and the M target gain values, each target gain value of the M target gain values corresponds to a first feature value of the N first feature values, and M is a positive integer less than or equal to N;

respectively processing corresponding first feature values based on the M target gain values to obtain M second feature values; and

performing quantization and entropy encoding on at least one processed first feature map to obtain encoded data, wherein the at least one processed first feature map comprises the M second feature values;

wherein the target mapping relationship comprises a target function mapping relationship, and based on an input of the target function mapping relationship comprising the target compression bit rate, an output of the target function mapping relationship comprises the M target gain values.

16. The device according to claim 15 , wherein the M second feature values are obtained by separately performing a multiplication operation on the M target gain values and the corresponding first feature values.

17. The device according to claim 15 , wherein the at least one first feature map comprises a first target feature map, the first target feature map comprises P first feature values, all of the P first feature values correspond to a same target gain value of the M target gain values, and P is a positive integer less than or equal to M.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 31, 2022
From: WANG, JING; CUI, ZE; BAI, BO
To: HUAWEI TECHNOLOGIES CO., LTD.
Reel/Frame 061599/0556 →
Priority Claims (1)
CN 202010082808.4 · Feb 7, 2020 · national
Continuity (2)
Continuation PCTCN2021075405 · Feb 5, 2021
Related Publication 20220375133A1 · Nov 24, 2022
References Cited (42)
US 20180174052A1 · Rippel · 2018 [cited by examiner]
US 20180316931A1 · Ho et al. · 2018 [cited by applicant]
US 20190132591A1 · Zhang et al. · 2019 [cited by applicant]
US 20190266490A1 · Rippel · 2019 [cited by examiner]
US 20190342551A1 · Zhu · 2019 [cited by examiner]
US 20200027247A1 · Minnen · 2020 [cited by examiner]
US 20200252611A1 · Li · 2020 [cited by examiner]
US 20220224926A1 · Lu et al. · 2022 [cited by applicant]
CN 102186076A · 2011 [cited by applicant]
CN 102378991A · 2012 [cited by applicant]
CN 103903271A · 2014 [cited by applicant]
CN 108028941A · 2018 [cited by applicant]
CN 109996066A · 2019 [cited by applicant]
CN 109996071A · 2019 [cited by applicant]
CN 110222717A · 2019 [cited by applicant]
CN 110222758A · 2019 [cited by applicant]
CN 110225342A · 2019 [cited by applicant]
JP 2021535689A · 2021 [cited by applicant]
WO 2018221863A1 · 2018 [cited by applicant]
WO 2020018985A1 · 2020 [cited by applicant]
WO 2022155245A1 · 2022 [cited by applicant]
Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling, Tong Chen et al., arXiv, 2019, pp. 1-13 (Year: 2019). [cited by examiner]
An enhanced entropy coding scheme for HEVC, Min Gao et al., Elsevier, 2018, pp. 108-123 (Year: 2018). [cited by examiner]
Learning a Deep Vector Quantization Network for Image Compression, Xiaotong Lu et al., IEEE, 2019, pp. 118815-118825 (Year: 2019). [cited by examiner]
Variable Rate Deep Image Compression With a Conditional Autoencoder, Yoojin Choi et al., ICCV, 2019, pp. 3146-3154 (Year: 2019). [cited by examiner]
Adaptive Downsampling to Improve Image Compression at Low Bit Rates, Weisi Lin et al., IEEE, 2006, pp. 2513 to 2521 (Year: 2006). [cited by examiner]
Adaptive Quantization Parameter Cascading in HEVC Hierarchical Coding, Tiesong Zhao et al., IEEE, 2016, pp. 2997-3009 (Year: 2016). [cited by examiner]
End-To-End Optimized Image Compression, Johannes Balle' et al., arXiv, 2017, pp. 1-27 (Year: 2017). [cited by examiner]
Non-local Attention Optimized Deep Image Compression, Haojie Liu et al., arXiv, 2019, pp. 1-10 (Year: 2019). [cited by examiner]
Duan et al., “Content-aware Deep Perceptual Image Compression,” IEEE, Total 6 pages, Institute of Electrical Electronics Engineers, New York, New York (Oct. 23, 2019). [cited by applicant]
Shoa et al., “Variable Length Coding for Fixed Rate, Low Latency, Low Complexity Compression Applications,” Data Compression Conference, Total 1 page (Apr. 3, 2008). [cited by applicant]
Chen et al., “Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling,” arXiv:1910.06244v1 [eess.IV], Total 13 pages (Oct. 11, 2019). [cited by applicant]
Choi et al., “Variable Rate Deep Image Compression With a Conditional Autoencoder,” Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Total 9 pages (2019). [cited by applicant]
Toderici et al., “Full Resolution Image Compression with Recurrent Neural Networks,” arXiv:1608.05148v2 [cs.CV], Total 9 pages (Jul. 7, 2017). [cited by applicant]
Zhen-Shan et al., “Research on image compression encryption algorithm based on chaos algorithm and compression perception,” Microelectronics & Computer vol. 37, No. 2, Total 4 pages (Feb. 2020). With the English Abstrac… [cited by applicant]
Akbari et al., “Learned Variable-Rate Image Compression With Residual Divisive Normalization,” arXiv:1912.05688v1 [eess.IV], Total 6 pages (Dec. 11, 2019). [cited by applicant]
Johnston et al., “Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks,” arXiv:1703.10114v1 [cs.CV], Total 9 pages (Mar. 29, 2017). [cited by applicant]
Theis et al., “Lossy Image Compression With Compressive Autoencoders,” arXiv:1703.00395v1 [stat.ML], Total 19 pages (Mar. 1, 2017). [cited by applicant]
Yang et al., “Variable Rate Deep Image Compression with Modulated Autoencoder,” Journal of Latex Class Files, vol. 14, No. 8, arXiv:1912.05526v1 [eess.IV] Dec. 11, 2019, Total 5 pages (Aug. 2019). [cited by applicant]
Toderici et al., “Variable Rate Image Compression With Recurrent Neural Networks,” Published as a conference paper at ICLR 2016, arXiv:1511.06085v5 [cs.CV], Total 13 pages (Mar. 1, 2016). [cited by applicant]
Balle et al., “Variational Image Compression With a Scale Hyperprior,” Published as a conference paper at ICLR 2018, arXiv:1802.01436v2 [eess.IV], Total 23 pages (May 1, 2018). [cited by applicant]
Chen et al., “Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling,” arXiv.org, https://arxiv.org/pdf/1910.06244, Total 13 pages (Oct. 11, 2019). [cited by applicant]