Advanced Learning-Based Inter Prediction for Future Video Coding

Chuanmin Jia; Shanshe Wang; Siwei Ma; Wenhong Duan; Yanchen Zhao

REVIEW 3 major objections 5 minor 30 references

Advanced Learning-Based Inter Prediction for Future Video Coding

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A 64-parameter network replaces a hand-tuned video filter

desk verdict A clean engineering contribution with a weak empirical core: the learned INTERPF replacement is practical and fast, but the reported coding gains are statistically fragile. read the letter →

arxiv 2411.15759 v1 pith:NGPJ5JH4 submitted 2024-11-24 cs.MM cs.CV

Yanchen Zhao , Wenhong Duan , Chuanmin Jia , Shanshe Wang , Siwei Ma This is my paper · ORCID

classification cs.MMcs.CV

keywords videocodinginterpredictionlearning-basedfilteringcomplexityreductionAVS4INTERPFBD-rateneuralnetworkinference

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to show that the handcrafted Inter Prediction Filter (INTERPF) in the AVS4 video standard can be replaced by a tiny learned function without hurting practical codec complexity. The proposed low-complexity learning-based inter prediction (LLIP) is a two-layer fully connected network that takes the same neighboring reconstructed pixels and, in one variant, the pixel's coordinates, and outputs the filtered prediction. Weights and biases are exported as floating-point numbers and inference runs in plain C++, so no deep-learning runtime is needed inside the codec. On the AVS4 exploration codec, LLIP reports small average BD-rate savings of about 0.01% for luma and 0.31% and 0.25% for the two chroma components under random-access coding, while encoding and decoding time stay near the anchor. If right, the result matters because it offers a practical path for putting learned tools into video standards with almost no complexity overhead.

What carries the argument

The load-bearing object is a two-layer fully connected network whose first layer has the same number of neurons as the input (5 for Scheme 1, 7 for Scheme 2), followed by a ReLU and a single output neuron. Its parameters are exported as single-precision weights and biases -- 30 weights plus 6 biases for Scheme 1, 56 weights plus 8 biases for Scheme 2 -- and implemented as direct CPU multiply-add operations, costing 30 or 56 MACs per pixel. The coordinates $(x,y)$ of the current pixel and per-component, per-resolution training are what separate the gaining variant from the non-gaining one; without coordinates, the learned filter only matches INTERPF. Training data come from exporting the pixels, coordinates, and block geometry used by the traditional INTERPF and labeling each with the original pixel value.

What would settle it

Retrain the same 64-parameter network on data produced by encoding with LLIP already replacing INTERPF, then measure BD-rate against the same anchor; if the gains do not stay at or improve on the reported 0.01%, 0.31%, and 0.25% savings, the out-of-loop training setup is not a faithful proxy for deployment.

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Extended reading notes

Core claim

The central claim is that the traditional INTERPF's fixed weighted-average equations can be replaced by a learned function $O(x,y)=f(I;\theta)$ with only 36 or 64 parameters per model. Scheme 1, which feeds only five pixel values, matches but does not beat the handcrafted filter; Scheme 2, which adds the current pixel's coordinates and trains separate models for luma, chroma, and resolution classes, produces the reported gains. The learned function is trained by regressing the filter's output toward the original uncoded pixel with mean-squared error, using data exported from encoding with the traditional filter. The paper reports average BD-rate savings of 0.01% for Y, 0.31% for U, and 0.25% for V under the random-access configuration, with encoding time 102-105% and decoding time 101-108% of the anchor.

Load-bearing premise

The network is trained on inputs produced by the old handcrafted filter, and the paper assumes those learned weights stay optimal once the learned filter is deployed inside the codec and changes the reconstructed neighbors used as future inputs.

Editorial extensions

If this is right

If LLIP is correct, AVS4-style codecs can adopt a learned inter prediction filter with essentially no change to the filtering process beyond swapping Eq. 4 for the exported network, keeping encoder and decoder runtime near the anchor.
The reported gains imply that the handcrafted INTERPF weights are not optimal for chroma content: learned per-component models save 0.31% and 0.25% BD-rate on U and V while luma stays essentially neutral.
Because inference requires only 30-56 MACs per pixel and no third-party runtime, the approach is deployable on CPU-only encoder and decoder paths where deep-learning tools are normally considered too expensive.
The training recipe -- export filter inputs, regress to the original pixel, export weights -- is a template that could be applied to other statistical-prior tools inside the same codec.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper: because the gains concentrate in chroma, a natural follow-up is to test whether the learned filter is implicitly correcting cross-component statistics; training a single model on luma and chroma together would separate that effect.
Beyond the paper: the training set is generated under the old filter, so retraining with the learned filter in the loop is a direct test of whether the reported gains are an upper or lower bound; the paper does not analyze this distribution shift.
Beyond the paper: the per-sequence results vary widely (for example, one 720p sequence shows a 2.24% V savings while another shows a 0.40% loss), so average gains may not be stable across content classes; a per-sequence or fixed-QP analysis would show where the method can be trusted.
Beyond the paper: the same export-and-infer recipe could be applied to other handcrafted filters in the codec, but the coordinate dependence of Scheme 2 suggests that these tiny networks capture positional priors, not image content, so their gains may not scale with model size.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short take: this is a legitimate engineering contribution with a weak empirical core. The idea is simple: replace the handcrafted INTERPF equations in AVS4 with a two-layer fully connected network, train on BVI-DVC with the traditional filter as data generator, export the weights, and run inference in pure C++. That is a sensible and practical way to integrate a learned tool into a codec, and the complexity is genuinely low—56 MACs per pixel and no third-party library. The measured encoding/decoding time is close to the anchor, and the 17x speedup over Libtorch is believable because the comparison includes CPU-GPU transfer overhead.

What the paper does well: it reports the failure of Scheme 1 honestly, it ablates the coordinates and per-component/resolution models, and the implementation details are clear enough to reproduce the inference path. The citation pattern is normal for this subfield.

The soft spot is the headline number. Table IV shows a Y BD-rate of -0.01%, which is noise. The V average of -0.25% is dominated by Vidyo3 (-2.24%); excluding that one sequence the V gain nearly vanishes. The U average of -0.31% also hinges on a few sequences and has a +1.13% outlier in the opposite direction. No significance test or confidence interval is reported, so the claim that this filter 'achieves significant coding gains' is not supported. The training/inference distribution shift is real but secondary: the end-to-end test already contains the shift, so the measured comparison is valid; the concern is whether the model is optimal, not whether the experiment is fair.

The paper would be stronger with per-sequence statistical analysis, a release of the six models, and an explicit statement that the luma gain is negligible. The luma result alone is enough to say the practical benefit is mostly in chroma, which is a much smaller part of the RD cost. Still, the engineering contribution and the honest negative result for Scheme 1 make this a reasonable paper for the video coding community. I would send it to peer review, but the reviewers should ask for significance testing and model release.

I would bring it to a reading group as an example of how to deploy a tiny learned tool in a codec, but I would not cite the specific BD-rate numbers.

Referee Report

3 major / 5 minor

Summary. The paper proposes LLIP, a low-complexity learning-based inter prediction method that replaces the handcrafted INTERPF in the AVS4 reference software EVM-0.4. A two-layer fully connected network with 36 or 64 exported parameters is trained on data generated by encoding BVI-DVC sequences with the traditional INTERPF, using the original pixels as regression targets. The inference is implemented in pure C++ without third-party dependencies. Experiments under the RA configuration report average BD-rate savings of 0.01% (Y), 0.31% (U), and 0.25% (V) relative to the EVM-0.4 anchor, with encoding and decoding times near the anchor (EncT 102-105%, DecT 101-108%).

Significance. If the reported gains were robust, the paper would make a useful practical contribution by showing that a tiny fully connected network with exported weights can replace a handcrafted coding tool at negligible complexity cost, and the proposed dependency-free inference library is a sensible engineering step for integrating neural tools into codecs. However, the central empirical claim is currently not established: the luma gain is statistically indistinguishable from zero, the chroma averages are driven by a few outlier sequences, and no significance testing is provided. The complexity speedup framing is also misleading. The method itself is not conceptually novel, but the integration approach is of interest to the AVS standardization community.

major comments (3)

[Table IV and Abstract] The average BD-rate gains cited in the abstract and Section I are not statistically robust. The luma average is -0.01%, which is effectively zero. The V average of -0.25% is dominated by the single sequence Vidyo3 (-2.24%); excluding it leaves roughly -0.07%. The U average of -0.31% also shows high variance, including a positive outlier (Crew +1.13%) and a large negative outlier (Vidyo3 -1.63%). No confidence intervals, significance tests, or per-sequence variance analysis are reported. Since these averages are the primary evidence for the claim that LLIP can replace INTERPF, the central empirical claim is not supported by the data as presented.
[Section IV-C, Table V] The complexity comparison is conducted on a single 416x240 sequence at a single QP (45), and the '17x encoding speedup' is computed against Libtorch with GPU data transfer, not against the coding complexity of the codec. The relevant comparison is between the proposed library (310.9 s) and the anchor (299.9 s), which is about 3.7% overhead and consistent with the EncT of 103% reported in Table IV. The speedup framing should be revised to avoid overstating the complexity advantage, and the comparison should be performed across multiple sequences and QPs.
[Section IV-A and Eq. (8)] The training data are generated by encoding BVI-DVC sequences with the traditional INTERPF active, and the model is trained with MSE against the original pixel values. At inference time, the filter output changes the reconstructed samples used as future references, so the input distribution differs from the training distribution. While the end-to-end experiment includes this shift, the paper provides no analysis of its effect on the optimality of the learned parameters or on the generalization of the reported gains. At minimum, a comparison of training and inference input statistics, or an ablation with the network trained on data from a codec that already uses the learned filter, should be discussed.

minor comments (5)

[Section IV-A heading] The heading reads 'Traning Process'; it should be 'Training Process'.
[Eq. (6)] The input vector I2 lists R5 in addition to R1, R2, R3, R4, P, x, y, but only four adjacent reconstructed pixels are defined in Fig. 1 and Table I gives the input dimension as 7. This is inconsistent; remove R5 or clarify what it represents.
[Table V] The table compares a GPU-based Libtorch implementation with a CPU-only proposed library and a CPU-only anchor, but the hardware configurations differ. State explicitly in the table caption that the Libtorch row uses a GPU and the proposed/anchor rows use CPU only, so the reader can interpret the absolute times correctly.
[Section II] The gains of the traditional INTERPF (0.34%, 0.04%, 0.13%) are cited from an AVS meeting contribution; providing the test conditions or a pointer to a public document would make the comparison with Table IV more transparent.
[Section I Contributions] The first contribution claims 'significant coding gains', but the measured luma BD-rate gain is -0.01%. The wording should be tempered to reflect the actual magnitudes and the chroma-only nature of the gains.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the LLIP network is trained on BVI-DVC data to predict original pixels, and the reported coding gains are measured on held-out AVS test sequences.

full rationale

The paper's derivation chain is empirical rather than circular. The network parameters are fitted by minimizing MSE between f(I;θ) and the original pixel G (Eq. 8) on BVI-DVC Class B/C/D videos, and the claimed result is a BD-rate comparison against EVM-0.4 on AVS CTC sequences (Tables III and IV) that are separate from the training corpus. The output O(x,y)=f(I;θ) in Eq. 7 replaces the handcrafted weighted average in Eq. 4, but the network is not trained to reproduce Eq. 4's output; its target is the original uncompressed pixel, so the coding gain is not an identity or a fitted parameter renamed as a prediction. The only mild concern is a train/deployment distribution shift — training inputs are exported from an anchor codec using the traditional INTERPF, while deployment changes the filter output and therefore future reconstructed neighbors — but this is a generalization/correctness issue, not a circularity of the kind where the prediction reduces to the input by construction. Background self-citations ([17], [18]) are not load-bearing, and the INTERPF baseline is defined by the equations in Section II rather than by appeal to authority. The statistical fragility of the luma gain (0.01%) and the outlier-driven V average are empirical robustness concerns, but they do not make the derivation circular.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central result is entirely empirical; the learned filter parameters are fitted, and performance rests on several unstated assumptions about the training data, the loss function, and generalization. The paper provides no code, model weights, or significance analysis.

free parameters (2)

Learned weights and biases for the six Scheme 2 models = Not provided; 64 parameters per model, 384 total
The reported U/V BD-rate gains are produced by these fitted parameters; without them the method reduces to the anchor. The paper does not disclose the numerical values.
Training schedule and batch size = Initial LR 1e-4, final LR 3e-5, 3 epochs, batch size 1000
Chosen by hand and not justified; they influence the learned filter and therefore the measured gains.

assumptions (4)

domain assumption Four adjacent reconstructed pixels plus the current prediction (and coordinates in Scheme 2) are sufficient inputs for a good inter prediction filter.
Defines the network input in Eq. 5 and Eq. 6; no ablation shows what additional context would be needed.
domain assumption Minimizing MSE to the original pixel G is a suitable proxy for rate-distortion performance.
Eq. 8 ignores bitrate cost, the on/off decision, and the fact that the filter output affects future reconstructions.
domain assumption BVI-DVC Classes B, C, D at QPs 27/32/38/45 generalize to the AVS CTC test sequences.
Section IV-A; no cross-validation or domain analysis is reported.
domain assumption The reconstructed-neighbor distribution at inference time matches the training distribution despite the filter change.
Training data is generated under the traditional INTERPF, while deployment uses the learned filter; the paper does not analyze this shift.

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Cite this review

Pith. "Pith review of Advanced Learning-Based Inter Prediction for Future Video Coding." pith.science (2026). https://pith.science/paper/NGPJ5JH4

@misc{pith2026241115759,
  author       = {Pith},
  title        = {Pith review of: Advanced Learning-Based Inter Prediction for Future Video Coding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NGPJ5JH4}},
  note         = {Machine review of arXiv:2411.15759}
}

read the original abstract

In the fourth generation Audio Video coding Standard (AVS4), the Inter Prediction Filter (INTERPF) reduces discontinuities between prediction and adjacent reconstructed pixels in inter prediction. The paper proposes a low complexity learning-based inter prediction (LLIP) method to replace the traditional INTERPF. LLIP enhances the filtering process by leveraging a lightweight neural network model, where parameters can be exported for efficient inference. Specifically, we extract pixels and coordinates utilized by the traditional INTERPF to form the training dataset. Subsequently, we export the weights and biases of the trained neural network model and implement the inference process without any third-party dependency, enabling seamless integration into video codec without relying on Libtorch, thus achieving faster inference speed. Ultimately, we replace the traditional handcraft filtering parameters in INTERPF with the learned optimal filtering parameters. This practical solution makes the combination of deep learning encoding tools with traditional video encoding schemes more efficient. Experimental results show that our approach achieves 0.01%, 0.31%, and 0.25% coding gain for the Y, U, and V components under the random access (RA) configuration on average.

Figures

Figures reproduced from arXiv: 2411.15759 by the authors.

**Figure 2.** Illustration of the low complexity learning-based inter prediction network architecture. The figure shows two different [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

30 extracted references · 28 canonical work pages

[1]

MPEG-2 video compression,

P. Tudor, “MPEG-2 video compression,” Electronics & Communication Engineering Journal, vol. 7, no. 6, pp. 257–264, 1995

work page 1995
[2]

Digital video: an intro- duction to MPEG-2,

B. G. Haskell, A. Puri, and A. N. Netravali, “Digital video: an intro- duction to MPEG-2,” Springer Science & Business Media , 1996

work page 1996
[3]

The MPEG-4 video standard verification model,

T. Sikora, “The MPEG-4 video standard verification model,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 7, no. 1, pp. 19–31, 1997

work page 1997
[4]

Overview of fine granularity scalability in MPEG-4 video stan- dard,

W. Li, “Overview of fine granularity scalability in MPEG-4 video stan- dard,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 11, no. 3, pp. 301–317, 2001

work page 2001
[5]

The H.264/A VC advanced video coding standard: Overview and introduction to the fidelity range extensions,

G. J. Sullivan, P. N. Topiwala, and A. Luthra, “The H.264/A VC advanced video coding standard: Overview and introduction to the fidelity range extensions,” Applications of Digital Image Processing XXVII , vol. 5558, pp. 454–474, 2004

work page 2004
[6]

Overview of the stereo and multiview video coding extensions of the H.264/MPEG-4 A VC standard,

A. Vetro, T. Wiegand, and G. J. Sullivan, “Overview of the stereo and multiview video coding extensions of the H.264/MPEG-4 A VC standard,” Proceedings of the IEEE , vol. 99, no. 4, pp. 626–642, 2011

work page 2011
[7]

Overview of the High Efficiency Video Coding (HEVC) standard,

G. J. Sullivan, J.-R. Ohm, W.-J. Han, and T. Wiegand, “Overview of the High Efficiency Video Coding (HEVC) standard,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 22, no. 12, pp. 1649– 1668, 2012

work page 2012
[8]

Overview of the Versatile Video Coding (VVC) standard and its applications,

B. Bross, Y .-K. Wang, Y . Ye, S. Liu, J. Chen, G. J. Sullivan, and J.-R. Ohm, “Overview of the Versatile Video Coding (VVC) standard and its applications,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 10, pp. 3736–3764, 2021

work page 2021

Show all 30 references

[9]

Framework of A VS2-video coding,

Z. He, L. Yu, X. Zheng, S. Ma, and Y . He, “Framework of A VS2-video coding,” in International Conference on Image Processing. IEEE, 2013, pp. 1515–1519

2013
[10]

Recent development of A VS video coding standard: A VS3,

J. Zhang, C. Jia, M. Lei, S. Wang, S. Ma, and W. Gao, “Recent development of A VS video coding standard: A VS3,” in Picture Coding Symposium (PCS) . IEEE, 2019, pp. 1–5

2019
[11]

An overview of core coding tools in the A V1 video codec,

Y . Chen, D. Murherjee, J. Han, A. Grange, Y . Xu, Z. Liu, S. Parker, C. Chen, H. Su, U. Joshi et al. , “An overview of core coding tools in the A V1 video codec,” in Picture Coding Symposium (PCS) . IEEE, 2018, pp. 41–45

2018
[12]

A technical overview of A V1,

J. Han, B. Li, D. Mukherjee, C.-H. Chiang, A. Grange, C. Chen, H. Su, S. Parker, S. Deng, U. Joshi et al. , “A technical overview of A V1,” Proceedings of the IEEE , vol. 109, no. 9, pp. 1435–1462, 2021

2021
[13]

Advanced geometric-based inter prediction for versatile video coding,

H. Gao, R.-L. Liao, K. Reuz ´e, S. Esenlik, E. Alshina, Y . Ye, J. Chen, J. Luo, C.-C. Chen, H. Huang et al. , “Advanced geometric-based inter prediction for versatile video coding,” in Data Compression Conference (DCC). IEEE, 2020, pp. 93–102

2020
[14]

Hybrid video cod- ing with trellis-coded quantization,

H. Schwarz, T. Nguyen, D. Marpe, and T. Wiegand, “Hybrid video cod- ing with trellis-coded quantization,” in Data Compression Conference (DCC). IEEE, 2019, pp. 182–191

2019
[15]

High throughput cabac entropy coding in HEVC,

V . Sze and M. Budagavi, “High throughput cabac entropy coding in HEVC,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 12, pp. 1778–1791, 2012

2012
[16]

Adaptive loop filtering for video coding,

Tsai, C.-Y ., Chen, Yamakage, T., Chong, and S. I., “Adaptive loop filtering for video coding,” IEEE Journal of Selected Topics in Signal Processing, 2013

2013
[17]

Image and video compression with neural networks: A review,

S. Ma, X. Zhang, C. Jia, Z. Zhao, S. Wang, and S. Wang, “Image and video compression with neural networks: A review,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 30, no. 6, pp. 1683– 1698, 2019

2019
[18]

Learned image compression using cross-component attention mecha- nism,

W. Duan, Z. Chang, C. Jia, S. Wang, S. Ma, L. Song, and W. Gao, “Learned image compression using cross-component attention mecha- nism,” IEEE Transactions on Image Processing , 2023

2023
[19]

Context-adaptive neural network-based prediction for image compression,

T. Dumas, A. Roumy, and C. Guillemot, “Context-adaptive neural network-based prediction for image compression,” IEEE Transactions on Image Processing , vol. 29, pp. 679–693, 2019

2019
[20]

Combined neural network-based intra prediction and transform selection,

T. Dumas, F. Galpin, and P. Bordes, “Combined neural network-based intra prediction and transform selection,” in Picture Coding Symposium (PCS). IEEE, 2021, pp. 1–5

2021
[21]

Enhanced motion-compensated video coding with deep virtual reference frame generation,

L. Zhao, S. Wang, X. Zhang, S. Wang, S. Ma, and W. Gao, “Enhanced motion-compensated video coding with deep virtual reference frame generation,” IEEE Transactions on Image Processing , vol. 28, no. 10, pp. 4832–4844, 2019

2019
[22]

Deep reference frame generation method for VVC inter prediction enhancement,

J. Jia, Y . Zhang, H. Zhu, Z. Chen, Z. Liu, X. Xu, and S. Liu, “Deep reference frame generation method for VVC inter prediction enhancement,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 34, no. 5, pp. 3111–3124, 2024

2024
[23]

Idam: Iteratively trained deep in-loop filter with adaptive model selection,

Y . Li, L. Zhang, and K. Zhang, “Idam: Iteratively trained deep in-loop filter with adaptive model selection,” ACM Transactions on Multimedia Computing Communications and Applications , vol. 19, no. 1, 2023

2023
[24]

Video compression based on jointly learned down-sampling and super-resolution networks,

Y . Wei, L. Chen, and L. Song, “Video compression based on jointly learned down-sampling and super-resolution networks,” in International Conference on Visual Communications and Image Processing (VCIP) . IEEE, 2021, pp. 1–5

2021
[25]

Super resolution with luma component enhancement for down-up sampling based video coding in VVC,

J. Ye, Q. Zhou, Q. Liu, K. Wu, X. Li, Y . Zhu, and C. Zhou, “Super resolution with luma component enhancement for down-up sampling based video coding in VVC,” in Data Compression Conference (DCC) . IEEE, 2024, pp. 599–599

2024
[26]

Inter prediction filtering,

W. Xu, Y . Zhao, and H. Yang, “Inter prediction filtering,” in 69th A VS Meeting, Chengdu, China, A VS M4821 , 2019

2019
[27]

BVI-DVC: a training database for deep video compression,

D. Ma, F. Zhang, and D. Bull, “BVI-DVC: a training database for deep video compression,” IEEE Transactions on Multimedia , 2021

2021
[28]

A VS4 video exploration platform common test conditions,

X. Liu and Y . Wang, “A VS4 video exploration platform common test conditions,” in 88th A VS Meeting, Shenzhen, China, A VS N3776 , 2024

2024
[29]

Adam: A method for stochastic optimization,

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

2014 arXiv
[30]

EVM software maintenance report,

X. Liu and Y . Wang, “EVM software maintenance report,” in 89th A VS Meeting, Shaoxing, China, A VS M8326 , 2024

2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

MPEG-2 video compression,

P. Tudor, “MPEG-2 video compression,” Electronics & Communication Engineering Journal, vol. 7, no. 6, pp. 257–264, 1995

work page 1995

[2] [2]

Digital video: an intro- duction to MPEG-2,

B. G. Haskell, A. Puri, and A. N. Netravali, “Digital video: an intro- duction to MPEG-2,” Springer Science & Business Media , 1996

work page 1996

[3] [3]

The MPEG-4 video standard verification model,

T. Sikora, “The MPEG-4 video standard verification model,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 7, no. 1, pp. 19–31, 1997

work page 1997

[4] [4]

Overview of fine granularity scalability in MPEG-4 video stan- dard,

W. Li, “Overview of fine granularity scalability in MPEG-4 video stan- dard,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 11, no. 3, pp. 301–317, 2001

work page 2001

[5] [5]

The H.264/A VC advanced video coding standard: Overview and introduction to the fidelity range extensions,

G. J. Sullivan, P. N. Topiwala, and A. Luthra, “The H.264/A VC advanced video coding standard: Overview and introduction to the fidelity range extensions,” Applications of Digital Image Processing XXVII , vol. 5558, pp. 454–474, 2004

work page 2004

[6] [6]

Overview of the stereo and multiview video coding extensions of the H.264/MPEG-4 A VC standard,

A. Vetro, T. Wiegand, and G. J. Sullivan, “Overview of the stereo and multiview video coding extensions of the H.264/MPEG-4 A VC standard,” Proceedings of the IEEE , vol. 99, no. 4, pp. 626–642, 2011

work page 2011

[7] [7]

Overview of the High Efficiency Video Coding (HEVC) standard,

G. J. Sullivan, J.-R. Ohm, W.-J. Han, and T. Wiegand, “Overview of the High Efficiency Video Coding (HEVC) standard,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 22, no. 12, pp. 1649– 1668, 2012

work page 2012

[8] [8]

Overview of the Versatile Video Coding (VVC) standard and its applications,

B. Bross, Y .-K. Wang, Y . Ye, S. Liu, J. Chen, G. J. Sullivan, and J.-R. Ohm, “Overview of the Versatile Video Coding (VVC) standard and its applications,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 10, pp. 3736–3764, 2021

work page 2021