IP Library Granted Patent US 12,695,880
Granted Patent B2
US 12,695,880 · App. 18/679,527 · Granted Jul 28, 2026

Decoder-side inter prediction based on no reference visual quality

Inventors: Tae Meon Bae (McLean, VA); Esmael Hejazi Dinan (McLean, VA); Kalyan Goswami (Reston, VA)
Assignee: Ofinno, LLC
H04N19/154H04N19/124H04N19/137H04N19/147H04N19/176H04N19/18H04N19/52H04N19/70
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 12,695,880
App. No.
18/679,527
Filed
May 31, 2024
Granted
Jul 28, 2026
Kind
B2
Art Unit
2487
USPC
375/240.16
Abstract

A decoder decodes, from a bitstream for a current block, an indication of whether decoder-side inter prediction is enabled, and a reconstructed residual block. Based on the decoder-side inter prediction being enabled by the indication and for each respective motion information of a plurality of motion information, the decoder generates a reconstructed block based on: a prediction block generated using the respective motion information, and the reconstructed residual block. For each reconstructed block, the decoder determines a visual quality of the reconstructed block without using the current block as a reference. Based on the visual qualities of the reconstructed blocks, motion information for predicting the current block is determining from the plurality of motion information. The decoder reconstructs the current block based on the determined motion information.

Claims (62)

1 . A method comprising:

decoding, by a decoder and from a bitstream for a current block:

an indication of whether decoder-side inter prediction is enabled; and

a reconstructed residual block;

for each respective motion information of a plurality of motion information and based on the decoder-side inter prediction being enabled by the indication:

generating, by the decoder, a reconstructed block based on:

a prediction block generated using the respective motion information; and

the reconstructed residual block; and

determining, by the decoder, a visual quality of the reconstructed block without using the current block as a reference;

determining, by the decoder and based on the visual qualities of the reconstructed blocks, motion information from the plurality of motion information for predicting the current block; and

reconstructing, by the decoder, the current block based on the determined motion information.

2 . The method of claim 1 , wherein the motion information comprises a motion vector and a reference picture number.

3 . The method of claim 1 , wherein the motion information is determined based on the visual quality, among the visual qualities of the reconstructed blocks, with a highest visual quality.

4 . The method of claim 1 , wherein the visual quality of the reconstructed block is determined based on a visual parameter measurement index or a deep Learning for Blind Image Quality Assessment.

5 . The method of claim 1 , wherein the reconstructed residual block is decoded based on:

de-quantizing quantized transform coefficients of a residual block, received from the bitstream, to generate de-quantized transformed coefficients; and

inverse transforming the de-quantized transformed coefficients to generate the reconstructed residual block.

6 . The method of claim 1 , wherein the indication of whether decoder-side inter prediction is enabled comprises a one-bit flag.

7 . The method of claim 1 , wherein:

the indication being a first value indicates that the decoder-side inter prediction is enabled; and

the indication being a second value indicates that the decoder-side inter prediction is disabled and the motion information is signaled in the bitstream.

8 . A decoder comprising:

one or more processors; and

memory storing instructions that, when executed by the one or more processors, cause the decoder to:

decode from a bitstream for a current block:

an indication of whether decoder-side inter prediction is enabled; and

a reconstructed residual block;

for each respective motion information of a plurality of motion information and based on the decoder-side inter prediction being enabled by the indication:

generate a reconstructed block based on:

a prediction block generated using the respective motion information; and

the reconstructed residual block; and

determine a visual quality of the reconstructed block without using the current block as a reference;

determine, based on the visual qualities of the reconstructed blocks, motion information from the plurality of motion information for predicting the current block; and

reconstruct the current block based on the determined motion information.

9 . The decoder of claim 8 , wherein the motion information comprises a motion vector and a reference picture number.

10 . The decoder of claim 8 , wherein the motion information is determined based on the visual quality, among the visual qualities of the reconstructed blocks, with a highest visual quality.

11 . The decoder of claim 8 , wherein the visual quality of the reconstructed block is determined based on a visual parameter measurement index or a deep Learning for Blind Image Quality Assessment.

12 . The decoder of claim 8 , wherein the reconstructed residual block is decoded based on:

de-quantizing quantized transform coefficients of a residual block, received from the bitstream, to generate de-quantized transformed coefficients; and

inverse transforming the de-quantized transformed coefficients to generate the reconstructed residual block.

13 . The decoder of claim 8 , wherein the indication of whether decoder-side inter prediction is enabled comprises a one-bit flag.

14 . The decoder of claim 8 , wherein:

the indication being a first value indicates that the decoder-side inter prediction is enabled; and

the indication being a second value indicates that the decoder-side inter prediction is disabled and the motion information is signaled in the bitstream.

15 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a decoder, cause the decoder to:

decode from a bitstream for a current block:

an indication of whether decoder-side inter prediction is enabled; and

a reconstructed residual block;

for each respective motion information of a plurality of motion information and based on the decoder-side inter prediction being enabled by the indication:

generate a reconstructed block based on:

a prediction block generated using the respective motion information; and

the reconstructed residual block; and

determine a visual quality of the reconstructed block without using the current block as a reference;

determine, based on the visual qualities of the reconstructed blocks, motion information from the plurality of motion information for predicting the current block; and

reconstruct the current block based on the determined motion information.

16 . The non-transitory computer-readable medium of claim 15 , wherein the motion information comprises a motion vector and a reference picture number.

17 . The non-transitory computer-readable medium of claim 15 , wherein the motion information is determined based on the visual quality, among the visual qualities of the reconstructed blocks, with a highest visual quality.

18 . The non-transitory computer-readable medium of claim 15 , wherein the visual quality of the reconstructed block is determined based on a visual parameter measurement index or a deep Learning for Blind Image Quality Assessment.

19 . The non-transitory computer-readable medium of claim 15 , wherein the indication of whether decoder-side inter prediction is enabled comprises a one-bit flag.

20 . The non-transitory computer-readable medium of claim 15 , wherein:

the indication being a first value indicates that the decoder-side inter prediction is enabled; and

the indication being a second value indicates that the decoder-side inter prediction is disabled and the motion information is signaled in the bitstream.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 25, 2024
From: BAE, TAE MEON; DINAN, ESMAEL HEJAZI; GOSWAMI, KALYAN
To: OFINNO, LLC
Reel/Frame 068085/0032 →
Continuity (3)
Continuation 17543214 · Dec 6, 2021
Provisional Application 63121536 · Dec 4, 2020
Related Publication 20240323397A1 · Sep 26, 2024
References Cited (40)
US 7170933B2 · Kouloheris · 2007 [cited by examiner]
US 10958947B1 · Wei et al. · 2021 [cited by applicant]
US 11616962B2 · Xu · 2023 [cited by examiner]
US 20110096828A1 · Chen et al. · 2011 [cited by applicant]
US 20130272420A1 · Laroche et al. · 2013 [cited by applicant]
US 20140254680A1 · Ho · 2014 [cited by examiner]
US 20140307771A1 · Hemmendorff et al. · 2014 [cited by applicant]
US 20160261870A1 · Tu et al. · 2016 [cited by applicant]
US 20190297344A1 · Holland et al. · 2019 [cited by applicant]
US 20200014963A1 · Gogoi · 2020 [cited by applicant]
US 20200404297A1 · Sarwer et al. · 2020 [cited by applicant]
US 20210211739A1 · Andreopoulos et al. · 2021 [cited by applicant]
US 20220038732A1 · Jang et al. · 2022 [cited by applicant]
US 20220159291A1 · Salehifar · 2022 [cited by examiner]
US 20220248061A1 · Zhang et al. · 2022 [cited by applicant]
US 20220345697A1 · Choi et al. · 2022 [cited by applicant]
CN 113841395A · 2021 [cited by applicant]
CN 103891303A · 2024 [cited by applicant]
EP 4075807A1 · 2022 [cited by applicant]
WO 2009075766A2 · 2009 [cited by applicant]
Bosse et al.; a Deep Neural Network for Image Quality Assessment; 2016 IEEE International Conference on Image Processing (ICIP); Sep. 25-28, 2016; IEEE; Phoenix, AZ, USA. [cited by applicant]
Ghadiyaram et al.; a No-Reference Video Quality Predictor for Compression and Scaling Artifacts; 2017 IEEE International Conference on Image Processing (ICIP); Sep. 17-20, 2017; IEEE; Beijing, China. [cited by applicant]
Ma et al.; A novel non-reference image quality assessment algorithm; 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC); Dec. 15-17, 2017; IEEE; Chengdu, China. [cited by applicant]
Kim et al.; Deep CNN-Based Blind Image Quality Predictor; IEEE Transactions on Neural Networks and Learning Systems; Jun. 12, 2018; IEEE. [cited by applicant]
Kamp et al.; Fast Decoder Side Motion Vector Derivation for Inter Frame Video Coding; 2009 Picture Coding Symposium; May 6-8, 2009; IEEE; Chicago, IL, USA. [cited by applicant]
Chen et al.; From QoS to QoE: A Tutorial on Video Quality Assessment; IEEE Communications Surveys & Tutorials (vol. 17, Issue: 2, Secondquarter 2015); Oct. 22, 2014; IEEE. [cited by applicant]
Bae et al.; HEVC-Based Perceptually Adaptive Video Coding Using a DCT-Based Local Distortion Detection Probability Model; IEEE Transactions on Image Processing (vol. 25, Issue: 7, Jul. 2016); May 13, 2016; IEEE. [cited by applicant]
Sollinger et al.; Non-reference image quality assessment and natural scene statistics to counter biometric sensor spoofing; IET Biom., 2018, vol. 7 Iss.4, pp. 314-324; Mar. 9, 2018. [cited by applicant]
Liu et al.; No-Reference Image Quality Assessment Method Based on Visual Parameters; Journal of Electronic Science and Technology, vol. 17, No. 2, Jun. 2019, pp. 171-184. [cited by applicant]
Zhu; No-reference Video Quality Assessment and Applications; Dissertation submitted for the degree of Doctor of Nature Science (Dr. rer. nat.); May 8, 2014. [cited by applicant]
Sogaard et al.; No-Reference Video Quality Assessment using Codec Analysis; IEEE Transactions on Circuits and Systems for Video Technology; Feb. 3, 2015; IEEE. [cited by applicant]
Xu et al.; No-reference/blind image quality assessment: a survey; IETE Technical Review; Apr. 8, 2016. [cited by applicant]
Jiang et al.; On Derivation of Most Probable Modes for Intra Prediction in Video Coding; 2018 IEEE International Symposium on Circuits and Systems (ISCAS); May 27-30, 2018; IEEE; Florence, Italy. [cited by applicant]
Bianco et al.; On the Use of Deep Learning for Blind Image Quality Assessment; arXiv:1602.05531v5 [cs.CV]; Apr. 4, 2017. [cited by applicant]
Yang; Perceptual Quality Assessment for Compressed Video; A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Electrical Engineering (Signal and Image Processing);… [cited by applicant]
Romaniak et al.; Perceptual quality assessment for H.264/AVC compression; 2012 IEEE Consumer Communications and Networking Conference (CCNC); Jan. 14-17, 2012; IEEE; Las Vegas, NV, USA. [cited by applicant]
Liu et al.; RankIQA: Learning from Rankings for No-reference Image Quality Assessment; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017; Jul. 26, 2017. [cited by applicant]
Rouis et al.; Study of No-Reference Video Quality Metrics for HEVC Compression; Journal of Telecommunications & Information Technology, 2016, Issue 1; pp. 22-28. [cited by applicant]
Zhang et al.; Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network; IIEEE Transactions on Circuits and Systems for Video Technology ( vol. 30, Issue: 1, Jan. 2020); Dec. 14, 2018; IEEE. [cited by applicant]
Zhang et al.; The Unreasonable Effectiveness of Deep Features as a Perceptual Metric; arXiv:1801.03924v2 [cs.CV]; Apr. 10, 2018. [cited by applicant]