# DUALF-C: Disentangled Light Field Compression with Entropy-Aware Bitstream Generation

Soheib Takhtardeshir, Roger Olsson, Christine Guillemot, Mårten Sjöström

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Soheib Takhtardeshir, Roger Olsson, Christine Guillemot, Mårten Sjöström. DUALF-C: Disentangled Light Field Compression with Entropy-Aware Bitstream Generation. VCIP 2025 - International Conference on Visual Communications and Image Processing, Dec 2025, Klagenfurt, Austria. pp.1-5. hal-05327806

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# DUALF-C: Disentangled Light Field Compression with Entropy-Aware Bitstream Generation

Soheib Takhtardeshir\*†, Roger Olsson\*, Christine Guillemot† and Mårten Sjöström\*

\*Department of Computer and Electrical Engineering, Mid Sweden University, Sundsvall, Sweden

†INRIA, Rennes, France

Email: {firstname.lastname}@miun.se, {firstname.lastname}@inria.fr

**Abstract**—Recent advancements in learned light field (LF) image compression highlight the advantages of modeling spatial and angular redundancies using deep generative models. Among these, Variational Autoencoders (VAEs) have shown strong potential in learning compact latent representations of LF data. However, many existing approaches rely on simple uniform quantization and basic entropy coding, which limits compression efficiency in practical applications. This paper introduces DUALF-C, a lightweight and retraining-free compression pipeline that augments pretrained VAE-based LF compression models using structured post-encoding transformations. The proposed framework integrates bitplane slicing, latent channel reordering, non-uniform quantization, and patch-based vector quantization to improve bitrate efficiency while preserving reconstruction quality. Experimental evaluations demonstrate that DUALF-C significantly reduces bit-per-pixel (BPP) without degrading image quality, making it a practical solution for bandwidth-constrained immersive imaging systems.

**Index Terms**—Light field compression, variational autoencoder, quantization, feature reordering, vector quantization

## I. INTRODUCTION

Light field (LF) imaging captures spatial and angular scene information, enabling applications such as immersive rendering, depth estimation, and view synthesis [1], [2]. However, its high dimensionality imposes substantial storage and transmission demands [3].

Traditional LF compression often adapts video coding standards like HEVC, or frameworks such as JPEG Pleno, using pseudo-sequences, disparity compensation, or transform coding [4]–[6]. While effective, such methods lack scalability and adaptability to real-world LF statistics.

Learned compression offers data-driven representations via deep neural networks [7], [8]. Variational Autoencoders (VAEs) are especially suited for LF compression, as they disentangle spatial and angular features into compact latent codes [9], [10].

Despite progress, most VAE-based LF compressors still use uniform quantization and basic entropy coding, limiting practical efficiency [11], [12]. This opens opportunities

to integrate classical coding with modern latent frameworks.

We build on a VAE-based framework that disentangles spatial and angular LF features into two latent maps [9]. A later extension, referred to as DUALF-D, introduced dual hyperpriors to model the entropy of each latent more effectively [13]. In this work, we propose a fully post-encoding pipeline that applies entropy-optimized transformations without modifying or retraining the model. DUALF-D serves as the fixed pretrained baseline throughout our experiments.

Our contributions in DUALF-C extends DUALF-D to include four key post-processing modules: bitplane slicing for bit-level entropy shaping, channel reordering for progressive transmission, analytic non-uniform quantization aligned with latent priors, and patch-based vector quantization for exploiting local redundancy. These components can be composed into hybrid pipelines tailored to different compression goals.

Experiments show that DUALF-C reduces BPP while maintaining high quality, confirming that post-encoding enhancements can extend learned compression in bandwidth-limited scenarios.

## II. BACKGROUND & RELATED WORK

Variational Autoencoders (VAEs) have become a cornerstone of learned image compression, offering principled latent variable models for mapping images into compact representations [7], [11]. Their effectiveness is further enhanced by hyperpriors that enable adaptive entropy modeling, resulting in state-of-the-art compression performance for natural images. These approaches serve as the foundational architecture for many learned compression systems.

To extend these methods to light field (LF) data, recent works have adopted various strategies to manage angular redundancy while preserving spatial structure [14], [15]. Some treat LF images as pseudo-video sequences, applying temporal models to angular views, while others leverage multi-stream architectures to disentangle spatial and angular components. These strategies have improved the expressiveness of LF latent representations.

This work was supported by EU-MSCA Plenoptima project (956770), and EU-InterReg Aurora project IMMERSE (20366448).
Despite architectural innovations, many learned compression methods continue to rely on basic quantization and coding steps, typically uniform quantization followed by arithmetic or range coding [7], [12]. While sufficient for initial prototypes, these simple techniques leave substantial room for improvement, particularly for practical deployment in bandwidth-constrained environments.

Classical compression literature offers a rich set of tools that remain underexplored in deep models. These include non-uniform quantization techniques designed to match statistical priors [16], entropy-optimized vector quantization methods [17], and bitplane-based coding schemes for progressive transmission [18]. Integrating these tools with learned models offers a pathway to better rate-distortion performance without requiring architectural retraining.

This work draws inspiration from both domains, proposing a hybrid compression pipeline that leverages classical tools on the latent representations produced by a pretrained VAE-based LF compression model. Our approach highlights the complementary strengths of conventional and learned compression in building scalable and efficient systems.

### III. METHODOLOGY

This work enhances a pretrained variational autoencoder (VAE)-based compression model for light field (LF) images by introducing a modular, post-encoding pipeline. Unlike end-to-end learned schemes, our approach applies compression optimizations directly on the latent representations without altering the encoder or retraining the network. These post-processing steps are designed to exploit statistical structures in the latents while maintaining full compatibility with the original decoder.

We introduce four compression modules, each targeting a specific source of redundancy: (1) bit-level entropy, (2) inter-channel importance, (3) prior-matching quantization, and (4) patch-level similarity. Each module operates independently and is fully invertible, allowing flexible composition without loss of reconstruction fidelity.

#### A. Bitplane Slicing for Entropy Shaping

To reduce bit cost without added distortion, we apply bitplane slicing after quantization. Compatible with both uniform and non-uniform schemes, this technique decomposes each latent value into binary bitplanes, allowing entropy coding to exploit bit-level redundancy.

Since quantized latents follow a zero-centered Gaussian-like distribution, most values cluster near zero, resulting in high redundancy in the most significant bits (MSBs). We convert each value to an 8-bit fixed-point binary form and split the tensor into *n* bitplanes per channel.

Each bitplane is then compressed independently using a static entropy coder. While zlib or range coding suffice for prototyping, more efficient options like arithmetic coding or ANS are preferred in practice.

This binary decomposition concentrates entropy in fewer bitplanes, improving coding efficiency and enabling progressive decoding.

#### B. Latent Channel Reordering for Prioritization

To support progressive transmission, we reorder latent channels based on their reconstruction importance. Some channels carry critical structural cues, while others encode less relevant details. After quantization, uniform or non-uniform, we assess channel importance using metrics like entropy, variance, or mean absolute magnitude. Channels are sorted by significance, and the reordered latents are encoded accordingly. The permutation is sent as lightweight metadata to restore the original layout at the decoder.

Since reordering often introduces structured sparsity, we apply run-length encoding (RLE) to further reduce redundancy before entropy coding. This prioritization improves rate-distortion performance and supports efficient streaming under bandwidth constraints.

#### C. Analytic Non-Uniform Quantization Aligned with Latent Prior

To improve quantization efficiency beyond uniform binning, we replace uniform quantization with analytic non-uniform quantizers that align better with the prior distribution of latent values. VAE-based models often assume a zero-mean Gaussian or Laplacian prior over latents, which implies that most values cluster tightly around zero.

Instead of learning quantizers from data or histograms, we apply fixed analytic functions such as $\mu$-law or logarithmic quantizers that concentrate bins near the origin. During encoding, each latent value is transformed using the quantizer's forward mapping, discretized, and then entropy coded. At decoding, a deterministic inverse mapping is applied to restore the dequantized value. This quantizer design reduces entropy near the origin without introducing training overhead, thereby lowering bitrates while maintaining reconstruction quality.

#### D. Patch-Based Vector Quantization for Local Redundancy

To exploit local spatial and cross-channel redundancy within latent tensors, we apply vector quantization (VQ) at the patch level. Latent feature maps often contain recurring patterns that can be effectively clustered and represented using a compact codebook.

We divide the concatenated latent representation, composed of spatial and angular branches, into non-overlapping 3D patches (e.g., $2 \times 2 \times C$), where *C* is the number of channels. These patches are flattened and matched against a pre-computed codebook of representative vectors trained using K-means clustering. During encoding, each patch is replaced by the index of its nearest codeword. At decoding, patches are reconstructed via a simple codebook lookup. This operation greatly reduces transmission cost in high-dimensional latent spaces with internal redundancy.
Compression Strategies in DUALF-C

We define four compression strategies that operate on the latent representations produced by the encoder, referred to here as *post-encoding transformations*.<fnref n="1" /> These strategies can be evaluated independently or combined into hybrid pipelines to support different compression objectives and deployment scenarios.

**Strategy 1: Bitplane Slicing with Static Coding.**
This pipeline first applies uniform quantization to the latent representations. The quantized values are then sliced into 8-bit binary planes, which are individually compressed using static entropy coders. The objective is to enhance entropy shaping while preserving reconstruction fidelity. Bitplane slicing exposes bit-level redundancy and enables efficient compression with minimal added complexity or quantization loss.

**Strategy 2: Latent Channel Reordering.** After quantization, either uniform or non-uniform, the latent channels are ranked based on importance measures such as entropy or variance. The reordered channels are transmitted along with permutation metadata to ensure correct reconstruction. This method aims to improve rate allocation and support progressive transmission. By prioritizing high-impact channels, it enhances compression performance under bandwidth constraints and enables graceful degradation when only partial data is available.

**Strategy 3: Prior-Aware Non-Uniform Quantization.** This approach replaces uniform quantization with a fixed non-uniform quantizer, such as μ-law [19], to align quantization bins with the latent distribution. The quantized values are then compressed using static entropy coding and later reconstructed via a deterministic inverse mapping. Tailoring the quantizer to match a Gaussian-like prior reduces bitrate while maintaining reconstruction quality and sample consistency.

**Strategy 4: Patch-Based Vector Quantization.** The latent space is divided into 3D patches, and each patch is assigned to a representative codeword from a static vector quantization codebook. Instead of transmitting full-precision values, only the patch indices are stored and transmitted. This pipeline captures spatial redundancy using discrete patch-level representations. Patch-based clustering allows for significant compression by leveraging repetitive local patterns observed across spatial and angular views.

Hybrid Pipelines

While each Strategy can be applied independently, certain combinations are compatible and offer complementary benefits:

* **S1 + S2:** Uniform quantization with bitplane slicing and channel prioritization for progressive transmission and entropy shaping.

* **S3 + S2:** Prior-aware non-uniform quantization followed by channel reordering to improve both coding efficiency and progressive decoding.

* **S4 + S2:** Patch-based vector quantization combined with channel prioritization to exploit local redundancy and improve streaming flexibility.

These combinations allow DUALF-C to balance compression ratio, reconstruction fidelity, and decoding complexity under various deployment scenarios.

IV. RESULTS AND ANALYSES

We evaluate our compression strategies using a pretrained dual-branch VAE on light field images from the EPFL dataset. All post-encoding transformations are applied without retraining or modifying the encoder–decoder structure. We report bit-per-pixel (BPP), PSNR, and inference time to assess module-level impact on compression performance.

A. Experimental Protocol

We use the EPFL light field dataset [20], which provides 15×15 sub-aperture views at 625×434 resolution. To avoid vignetting and distortion in corner views, we crop each sample to the central 13×13 views. Each sample is processed by the pretrained VAE encoder [13] to produce a 128-channel latent tensor of spatial size 8×12. Post-encoding modules are applied to this tensor, followed by zlib-based entropy coding. We report BPP, PSNR, and runtime using consistent hardware [13]. This controlled setup isolates the effect of each transformation and ensures fair, reproducible comparisons.

B. Strategy -Level Evaluation

**Bitplane Slicing (S1)** enhances entropy shaping by decomposing quantized latents into binary planes. It achieves a 10–15% BPP reduction over uniform quantization, with negligible quality loss when all bitplanes are preserved. Discarding lower planes (e.g., retaining 6–7 MSBs) leads to a minor PSNR drop (0.2–0.4 dB). Runtime increases slightly due to per-plane entropy coding, but remains stable across scenes.

**Latent Channel Reordering (S2)** prioritizes informative latent channels by ranking them based on entropy or variance. On its own, this yields a modest BPP gain (2–5%), but significantly improves progressive decoding by allocating bits to critical structure early in the stream. For further compression, channels below a significance threshold can be omitted, and reordered tensors often exhibit structured sparsity that we exploit using run-length encoding (RLE). These enhancements introduce minimal runtime overhead while improving rate–distortion performance.

**Prior-Aware Non-Uniform Quantization (S3)** aligns quantization bins with the latent distribution using fixed analytic functions such as μ-law. Compared to uniform quantization, this reduces BPP by 10–15% and

<footnote n="1">¹In this context, "post-encoding" refers to transformations applied directly to the encoder's latent outputs, without modifying the encoder or decoder architecture.</footnote>
may increase PSNR by up to 0.5 dB, particularly in low-energy regions. The forward and inverse mappings are table-driven and fast, making this module highly efficient and training-free.

**Patch-Based Vector Quantization (S4)** captures spatial and cross-channel redundancy by clustering local 3D latent patches into a fixed codebook. This module delivers the largest single-method BPP savings, typically 15–25%, while preserving visual quality using a compact 256-codeword vocabulary. Larger codebooks can further enhance fidelity at high bitrates but come with additional runtime and memory costs. Although computationally more demanding due to nearest-neighbor search, it is particularly effective on repetitive scene structures and justifies the added latency in exchange for higher compression efficiency.

<figure><img src="image_2.png" /><figcaption>Fig. 1. Rate–Distortion (RD) comparison on the *Bikes* scene from the EPFL dataset. Methods include HEVC [21], JPEG-Pleno [6], HLVC [22], RLVC [23], Shi et al. [24], EF-VAE [9], DUALF-D [13], and our proposed S4+S2 pipeline.</figcaption></figure>

Figure 1 shows the rate-distortion performance on the *Bikes* scene for the S4+S2 pipeline, compared against classical and learned baselines. Our method achieves the highest PSNR at low bitrates and maintains parity with the strongest methods at higher rates. Similar gains were consistently observed across the full EPFL dataset.

### C. Hybrid Pipeline Performance

Hybrid configurations of the proposed modules deliver further gains. Their effectiveness depends on both the content structure and the compression goal:

* **S4+S2 (Patch VQ + Reordering)** achieves the best overall performance, yielding up to 31% BPP reduction with minimal PSNR loss and strong streaming performance.

* **S3+S2 (Non-Uniform + Reordering)** performs best for low-frequency or distribution-aligned scenes,

with up to 26% BPP gain and moderate decoding cost.

* **S1+S2 (Bitplane + Reordering)** offers the fastest decoding pipeline while supporting progressive streaming and moderate compression efficiency.

Despite the increase in computational cost, even the most complex hybrid (e.g., S4+S2) maintains a significantly faster inference time than recent learning-based compression methods such as HLVC, RLVC, and EF-VAE, due to its avoidance of deep decoding networks.

<table><caption>TABLE I
BITRATE REDUCTION AND RUNTIME BREAKDOWN ON DANGER
SCENE (PSNR = 33.2 dB)</caption><thead><tr><th>Pipeline</th><th>BPP Red.</th><th>Enc. (s)</th><th>Dec. (s)</th><th>Total (s)</th></tr></thead><tbody><tr><td>Baseline (DUALF-D)</td><td>0%</td><td><strong>4.00</strong></td><td><strong>0.81</strong></td><td><strong>4.81</strong></td></tr><tr><td>S1: Bitplane</td><td>12%</td><td>4.45</td><td>0.95</td><td>5.40</td></tr><tr><td>S2: Reordering</td><td>10%</td><td>4.10</td><td>0.85</td><td>4.95</td></tr><tr><td>S3: Non-Uniform Q</td><td>18%</td><td>4.40</td><td>0.90</td><td>5.30</td></tr><tr><td>S4: Patch VQ</td><td>22%</td><td>5.80</td><td>1.30</td><td>7.10</td></tr><tr><td>S1+S2</td><td>15%</td><td>4.35</td><td>0.90</td><td>5.25</td></tr><tr><td>S3+S2</td><td>26%</td><td>6.10</td><td>1.50</td><td>7.60</td></tr><tr><td>S4+S2</td><td><strong>31%</strong></td><td>5.90</td><td>1.45</td><td><strong>7.35</strong></td></tr></tbody></table>

Table I summarizes the compression gains and runtime costs for each method on the *Danger* scene. BPP reductions are measured at a fixed PSNR target of 33.2 dB to enable fair comparisons. While hybrid pipelines incur higher processing time than the baseline, they consistently outperform traditional and learning-based codecs in overall speed and compression performance, especially in low-bitrate regimes.

### D. Summary and Practical Implications

Our results show that structured post-encoding modules significantly improve compression efficiency when applied to a pretrained VAE-based LF model (DUALF-D). Without retraining, we reduce bitrate by up to 31% on EPFL data, with minimal visual quality loss and modest runtime increase. These lightweight methods suit practical scenarios where decoder simplicity and retraining avoidance are key. Hybrid pipelines offer tunable trade-offs between efficiency and complexity, supporting both streaming and offline modes.

## V. CONCLUSION

We introduced DUALF-C, a modular framework that enhances pretrained VAE-based LF compression via post-encoding transformations. By applying bitplane slicing, channel reordering, non-uniform quantization, and patch-based vector quantization, we improve compression performance without altering the model or retraining. Each module targets a distinct latent redundancy and supports flexible deployment scenarios. Experiments confirm that DUALF-C achieves lower bitrates and competitive visual quality at practical inference times. This post-training approach bridges learned and classical compression techniques and may extend to temporal LF data and perceptual tasks in immersive systems.
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