Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding

Belangrijkste concepten

Neural compression design using lattice transform coding approaches optimal vector quantization and improves one-shot coding performance.

Samenvatting

Neural compression has made significant progress, but sub-optimality arises in i.i.d. sequences due to scalar quantization. Lattice Transform Coding (LTC) overcomes this by employing lattice quantization, achieving optimal vector quantization. LTC enhances one-shot coding on general vector sources and enables block coding for i.i.d. sequences.

Statistieken

NTC with n = 2 achieves the same performance as ECSQ.
LTC improves one-shot coding performance on general vector sources.
LTC avoids exponential complexity of direct codebook search under VQ.
The gap between NTC performance and R(D) is attributed to lack of large block-lengths.
ECVQ directly enumerates quantization centers in the space of x.

Citaten

"NTC is unable to recover ECVQ for n = 2 and always achieves the same performance as ECSQ."
"LTC brings several challenges that require changes compared to NTC training."
"LTC is able to achieve optimal coding schemes for i.i.d. scalar sequences."

Belangrijkste Inzichten Gedestilleerd Uit

Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding

by Eric Lei,Ham... om arxiv.org 03-13-2024

https://arxiv.org/pdf/2403.07320.pdf

Diepere vragen

How can LTC be applied to other domains beyond neural compression

Lattice Transform Coding (LTC) can be applied to various domains beyond neural compression, especially in scenarios where efficient and effective quantization of high-dimensional data is required. One potential application is in image and video compression, where LTC can improve the rate-distortion performance by optimizing vector quantization at various dimensions. Additionally, LTC can be beneficial in sensor networks for compressing sensor data efficiently while maintaining a good trade-off between rate and distortion. In communication systems, LTC can enhance the performance of source coding by approaching the asymptotically-achievable rate-distortion function at reasonable complexity levels.

What are potential drawbacks or limitations of using lattice transform coding

While Lattice Transform Coding (LTC) offers several advantages in improving one-shot coding schemes and achieving optimal vector quantization on general sources, there are also potential drawbacks or limitations associated with its use. One limitation is the computational complexity involved in lattice quantization algorithms when dealing with high-dimensional data. Implementing efficient lattice decoders for large rates and dimensions may pose challenges due to the NP-hard nature of solving closest-vector problems for general lattices. Another drawback could be related to training stability issues that may arise when using straight-through estimation (STE) for distortion term computation during backpropagation.

How does companding theory relate to the principles behind LTC

Companding theory provides insights into how optimal companders based on lattice packing efficiency play a crucial role in achieving near-optimal vector quantization performance without requiring exponential codebook search complexities. The principles behind Companding theory relate to Lattice Transform Coding (LTC) as both approaches focus on optimizing encoding processes through non-linear transformations followed by efficient quantization methods such as lattice quantizers. By leveraging companding principles within LTC, it becomes possible to approach information-theoretic limits more effectively while addressing sub-optimality issues observed with traditional scalar or uniform scalar quantization methods commonly used in neural compression techniques like NTC.