代做CSEN 338/ECEN 641 Image and Video Compression代做C/C++编程

日期：2024-05-02 03:45

CSEN 338/ECEN 641 (4 Units)

Image and Video Compression

PROJECT INFORMATION AND LIST OF SUGGESTED PROJECTS

Phases:

•    Group or individual project

•    Literature survey, study

•    Design or Discussion or Comparison

•    Implementation if any

•    Presentation & Report

Suggested Topics:

1.   Entropy Coding or Decoding

•    Conduct  a literature survey on a fast entropy coding or decoding techniques (e.g. fast or parallel CABAC decoding or Golomb decoding).

•    Conduct a simple implementation (e.g. software in Python/C/Matlab or hardware design) of a simple entropy coder; test it out with images and evaluate your results (e.g. memory, complexity).

•    Do a presentation and write a report.

2.   Predictor or Quantizer Design

•    Conduct   literature  survey  on  good  predictor/interpolator/filter  techniques  (e.g.  AIF,  Wiener, directional) or good quantization techniques (e.g. Lloyd-Max or others, cascaded, adaptive, AQMS, RDOQ, subjective). It would be better (but not critical) if you take into account perceptual visual distortion, especially for quantization.

•    Produce  an  implementation  (e.g.  software  in  Python/C/Matlab  or  hardware  design)  of a  simple predictor or quantizer; test out your work with images.

•    Evaluate your results (e.g. bit-rate vs. visual quality vs. computational complexity).

•    Do a presentation and write a report.

3.   2-D Transform

•    Conduct literature survey on new 2-D or directional transform for image/video coding.

•    Produce an implementation & its inverse (e.g. Python/C/Matlab) for the transform; you can use Matlab Image Processing Tool Box; test your transform. with several images with quantization.

•    Evaluate your results (e.g. bit-rate vs. visual quality vs. computational/memory complexity).

•    Do a presentation and write a report.

4.   Design of a Motion Estimator / Compensator

•    Conduct literature survey and study on recent motion estimation techniques.

•    Investigate one issue for motion estimation (e.g. sub-pel, AIF filter, motion model, coding methods for MV or residue, reference picture selection/generation, search range, partial distortion search, MV competition,flexible search patterns, merge mode, affine methods, motion fields) or advanced motion methods in HEVC or VVC.

•    Design and produce a simple implementation to demonstrate motion estimation (e.g. in JM or HM or Matlab/C/Python); test out your design with two or more frames.

•    Evaluate your results (e.g. bit-rate vs. visual quality vs. computational/memory complexity).

•    Do a presentation and write a report.

5.   Performance Analysis for Image Codec

•     Study an image codec structure of the BPG standard.

•    You can also find out new techniques adopted in BPG that were not in JPEG.

•    Suggest a list of performance issues to evaluate (e.g. coding efficiency, computational complexity).

•    Do a presentation and write a report.

6.   Performance Analysis for Video Codec

•    Study  a  video  codec  or just  decoding. JVET VTM (VVC/H.266) is preferred, but JCT-VC HM (HEVC/H.265) or H.264 JM arefine. For some cases you need to know C++. Alternatively, you could explore 3D or scalable extensions of the codec (e.g. JCT-3V HTM (3D-HEVC) software).

•    You can also find out new techniques adopted in VTM that were not in HM, or in HM that are not in JM; or comparing two different codecs.

•    Suggest a list of performance issues to evaluate (e.g. coding efficiency and computational complexity). Alternatively, you could explore multicore processor or GPUs for speeding up codecs.

•    Do a presentation and write a report.

7.   Machine Learning Methods in Video Coding

•    Study a video coding technique and see which part(s) (e.g. mode decision, partitioning/coding unit depth decision, transform, intra prediction, motion estimation) can machine learning methods (e.g. SVM,  classification,  decision  trees,  PCA,  sparse  dictionary  learning,  K-SVD)  or  deep  learning methods (e.g. CNN, GAN, RNN, transformer) be applied to achieve better performance.

•    Suggest a list of performance issues to evaluate (e.g. coding efficiency, computational complexity).

•    Do a presentation and write a report.

8.   Deep Learning Methods in Video Coding

•    I am particularly interested in the study of one of these methods for end-to-end image/video coding:

•    (a) Use of convolutional neural network (CNN) in video coding.

•    (b) Use of generative adversarial network (GAN) in image/video coding.

•    (c) Use of autoencoder (AE), variational autoencoder (VAE), recurrent neural network (RNN),LSTM, and especially transformers, in image/video coding.

•    (d) Use of deep learning approaches in motion estimation.

•    (e) Use of reinforcement learning in video rate control.

•    (f) Visual quality metric for learned image/video coding.

•    (g) Complexity reduction methods.

•    Suggest a list of performance issues to evaluate (e.g. coding efficiency, computational complexity).

•    Do a presentation and write a report.

9.   A Current Hot Topic (typically 1 person but can be more) - Survey / Study / Comparison

Select a recently hot topic of interest from:

•    Latest and future JPEG image coding standards - JPEG-AI, JPEG-DNA, JPEG-NFT, JPEG-XE.

•    Latest and future video coding standards - VVC/H.266,NNVC, etc.

•    Video coding for HDR, WCG, 360 video, screen content, 3-D, point cloud, etc.

•    Visual volumetric video coding (V3C) standards - VPCC, MIV, V-DMC.

•    Video coding for surveillance video.

•    Video for visually impaired.

•    Image coding for plenoptic images - light field, point cloud, or holograph.

•    Visual quality metric for plenoptic images, 360 video, 3-D video, and point cloud.

•    Deep learning assisted tools for image/video coding (e.g. post-processing, optimization, rate control, reference frame. generation, intra prediction).

•    End-to-end deep learning-based image/video coding (e.g. autoencoder, transformer).

•    Generative AI approaches - GAN, diffusion probabilistic models, etc.

•    Deep learning in visual quality assessment.

•    Image or video coding for machines (VCM) (e.g. semantic coding, feature coding).

•    Green video coding.

•    Advanced  approaches  in  HVS  (e.g.  psycho-visual  studies,  DMOS,  JND,  SSIM,  saliency  map, reference-free method) and its effect on RDO and quantization.

•    Visual attention and saliency.

•    Methods in 3D video coding (e.g. depth coding, view synthesis, and 3D-HEVC).

•    Graphics compression (especially 3D graphics) or haptic compression or AR video.

•    Coding for immersive multimedia, VR/AR in metaverse.

•   Visual communication (e.g. transcoding, rate control or shaping, congestion control) over networks.

•    Advanced intra-prediction methods or inter-prediction methods for VVC or beyond.

•    RDO plus complexity, Lagrange multiplier, or advanced quantization methods.

•    Pre- or post-processing (e.g. artifact removal, denoising, or error concealment).

•    Computer vision approaches (e.g. face detection) in coding.

•    Parallel methods and/or use of GPUs or TPUs (systolic arrays) in video coding.

•    Other advanced topics (e.g. super-resolution).

•    Your suggestion (need approval, and must be related to compression).

   Conduct a literature survey on the topic.

   Study and compare different approaches, if needed.

   Do a presentation and write a report.

10. Your Suggestion

•    Needs approval.

•    Should  involve  study  or  design,  simple  implementation  or  comparison/evaluation,  report,  and presentation.

Note: Above are just suggestions, feel free to modify them or suggest your own topic to suit your projects.

Note: The use of AI tools such as ChatGPT is not allowed. Copying materials from websites is not allowed , but references are encouraged.

References

1.   The Internet and Web Sites.

2.   Books recommended on the syllabus and other related books.

3.   Industrial Magazines (for more industrial oriented projects).

4.   Journals  (IEEE  Transactions  on  Circuits  &  Systems  for  Video  Technology,  IEEE  Transactions  on Consumer Electronics, IEEE Transactions on Multimedia, IEEE Communications, IEEE Multimedia,

IEEE Signal Processing, IEEE Networks, etc.) (For more research oriented projects). 

5.   Conference proceedings.

Report

Suggestion (but not limited to):

•    Abstract of about 50 words

•    Literature survey

•    Design or Analysis or Study

•    Implementation or Comparison (if any)

•    Results or Presentation (if any)

•    Evaluation or Comparison

•    Conclusion (about 20 words)

•    List of References (must)

•    Appendices (if any)

It must be a technical report, not sale’stalk, user’s manual, or layman’stalk.

Your report must not exceed 4 pages (for 1 person), 6 pages (group of 2), 8 pages (group of 3), or 10 pages (group of 4). For each group project, please indicate the individual responsible for each portion of the work. Note that students in the same group may or may not receive the same grade.

Note also that project result is not the most important thing. I grade you based on your effort and work, how challenging the project is, and what you have learned. Please do not “copy and paste” materials from web or other literatures.  Please do not use AI tools such as ChatGPT to generate materials for the report.

Good Luck