Stanford EE274: Data Compression I 2023 I Lecture 18 - Video Compression

Video Compression

• Video compression aims to convert videos into a compressed bitstream.
• Video compression is computationally intensive, leading to the development of specialized hardware (e.g., Apple's M2 chip).
• Key video parameters include frame size (resolution) and frames per second (FPS).
• Video compression techniques are used to reduce the size of video files.
• Motion vectors are used to find the motion between frames.
• Block matching algorithms are used to find the best match for a block of pixels in a reference frame.
• Hierarchical searching is used to make block matching more efficient.
• The residual frame is the difference between the current frame and the predicted frame.
• The residual frame is encoded using image compression techniques.
• Different hyperparameters can be used for encoding the residual frame.
• B-frames (bilinear coding) are used in video encoding to improve compression by interpolating between I-frames (independent frames) and P-frames (predictive frames).
• I-frame compression is useful in video editing software as it allows for efficient editing of individual frames without the need to decode the entire video.
• Traditional video coding methods like H.264 and H.265 use block-based motion estimation and compensation, which can result in blocky artifacts, especially at lower bit rates.
• Learned image compression techniques can achieve smoother motion and fewer artifacts compared to traditional video codecs.
• Machine learning-based video codecs have the potential to outperform traditional codecs, but they are computationally expensive and may not be suitable for real-time applications.

Lossless Compression

• The course covered fundamental concepts such as entropy, prefix-free codes, and lossless compression techniques.
• Entropy provides a theoretical limit for lossless compression, and understanding entropy helps in practical compression scenarios.
• Non-IID data or real data was explored, including concepts like entropy rate, conditional entropy, and the relationship between good predictors and good compressors.
• Advanced predictors like context tree weighting (CTW) and prediction by partial matching (PPM) were discussed, along with the use of language models as powerful predictors for compression.
• Universal compressors achieve the entropy rate for any stationary source.
• Tips for using lossless compression in practice:
  • Evaluate the data and understand the standard compression methods.
  • Use existing tools and standard compressors.

Lossy Compression

• Scalar quantization, rate-distortion theory, vector quantization, and transform coding.
• Theory for lossy compression is less useful compared to lossless compression.
• Human perception plays a role in designing lossy compression algorithms.

Image Compression

• JPEG, DCT, and ML-based image compression.

Video Compression

• Residual coding, quantization, and lossless coding.

Other Topics in Information Theory

• Distributed compression and error correction coding.
• Succinct data structures for compressed data with random access.
• Compression for hardware and neural networks.
• Compression in specialized domains like AR/VR, genomics, and vision processing.

Stanford Compression Library

• Easy to use and experiment with.
• Helps understand arithmetic coding, ANS, and range coding.
• Resources on the website will be continuously updated.

Other Stanford Classes

• E276 Information Theory: Covers more theory, especially in lossy compression.
• E376 Topics in Information Theory: Focuses on universal schemes and proving entropy rates.
• Music 422: Explores audio coding, psychoacoustics, and human perception.
• CS 236 Generative Models in AI: Useful for understanding how to build good compressors through probabilistic and machine learning techniques.

Speaker's Journey in Compression

• Started in 2016 at Saki's lab.
• Will continue to work on compression-related research.