DEV Community: Ramesh Babu Anaparti

Challenges to adapt AI-based Video Codecs

Ramesh Babu Anaparti — Mon, 01 Sep 2025 04:28:00 +0000

Video data now dominates internet traffic, accounting for over 80% of total bandwidth consumption, with growth driven by streaming platforms (YouTube, Netflix, Prime Video etc.), social media (Facebook, Instagram etc.), video conferencing (Zoom, Teams, RingCentral etc.), and video surveillance systems. As demand rises, so does the need for more efficient video compression techniques to reduce bandwidth usage without sacrificing visual quality.

The Old Paradigm: Traditional Codecs

Traditional video codecs like H.264/AVC and H.265/HEVC rely on hand-engineered techniques such as:

Block-based motion estimation
Macro-blocking
Transform coding using Discrete Cosine Transform (DCT)
Entropy coding

These methods have been refined over decades, culminating in complex standards that offer high compression efficiency. However, each new generation such as H.266/VVC (Versatile Video Coding), increases in complexity, making implementation, optimization, and hardware support more challenging. Adoption of new standards often lags years behind standardization, partly due to this growing computational burden.

The New Paradigm: AI-Based Codecs

Recent advances in deep learning have paved the way for a new paradigm in video compression:

        Input video → Neural Network → Compressed Data

Instead of relying on fixed transforms and hand-designed heuristics, AI-based codecs learn to compress video data using data-driven models, often trained end-to-end. These models can adapt to content-specific patterns and exploit spatial-temporal redundancies more effectively.

Key characteristics of AI-based codecs:

Symmetric encoding and decoding: Unlike traditional codecs, where encoding is far more computationally intensive, AI models often have similar complexity for both operations.
Content-adaptive compression: Neural models can be fine-tuned or dynamically optimized based on the content type (Example: animation, sports, surveillance).
Potential for higher perceptual quality: Especially at low bitrates, AI codecs often outperform traditional ones in terms of subjective visual quality.

Current Research and Industry Efforts

Although AI-based codecs show promise, many are still in the research or early deployment stage. Real-time performance, hardware compatibility, and generalization remain open challenges.

Here are some notable initiatives in this space:

Microsoft DCVC-FM
Deep Context Video Compression with Feature Modulation. Delivers high compression efficiency, but not yet capable of real-time processing.
Apple / WaveOne ELF-VC
An advanced learned video codec. Strong performance, but again limited by high computational demand and real-time constraints.
Qualcomm NVC (Neural Video Codec)
Designed for real-time use with lower resource consumption. However, it currently lags behind in compression efficiency compared to heavier models.
Deep Render
A startup focused on deploying deep learning-based codecs in real-world applications, balancing compression gains with practical runtime constraints.

Challenges in Adopting AI-Based Video Codecs

While AI-powered video codecs promise major improvements in compression efficiency and perceptual quality, real-world adoption is still facing several critical challenges. Unlike traditional codecs, AI-based approaches introduce a new set of complexities ranging from model specialization to hardware dependencies and infrastructure costs.

1. One-Size-Fits-All Doesn't Work: The Need for Application-Specific Models

AI codecs are not general-purpose by default. Each application domain has distinct characteristics, and a single model may not perform optimally across all use cases. This requires either fine-tuning or training models specifically for different scenarios:

Video Conferencing
- Background is usually static (unless users enable virtual backgrounds).
- Foreground objects include faces, laptops, and other small gadgets.
- Requires high face clarity; often benefits from face-aware compression.
- Traditional codecs use long-term reference frames to exploit temporal redundancy; AI codecs must learn this implicitly.
Video Surveillance
- Static backgrounds, but lighting conditions vary across time.
- Foreground includes people, vehicles, and animals etc.
- Needs to preserve detail during event triggers (Example: motion).
- Models must adapt to varying lighting and compress efficiently during inactivity.
Video Streaming
- Highly diverse content: every scene can have a new background and subject.
- Requires models trained on large, diverse datasets to generalize well.
- Compression must balance bitrate, visual quality, and latency.

As a result, training generalized models or maintaining multiple specialized models per use case significantly increases development complexity and deployment overhead.

2. Hardware Limitations: The Need for NPUs and AI Acceleration

AI codecs are compute-intensive, especially during encoding and decoding. Unlike traditional codecs, which can run on general-purpose CPUs with hardware acceleration and dedicated video processing units, AI models typically require Graphics Processing Units (GPUs) or Neural Processing Units (NPUs) or similar dedicated AI accelerators.

Most current consumer devices smartphones, TVs, laptops lack sufficient on-device AI compute for real-time encoding/decoding.
NPUs are gradually being integrated into mobile SoCs, and widespread NPU availability is expected by 2030.
Until then, AI codec deployment at scale will remain limited to cloud or high-end edge devices, increasing cost and latency.

Adopting AI codecs at scale would also require a complete re- architecture of existing video infrastructure, including hardware encoders, decoders, and content delivery pipelines, which is an effort that comes with significant cost.

3. New Quality Metrics Needed: Traditional Metrics Fall Short

Compression quality in AI codecs is tightly linked to the underlying model architecture and training data. However, traditional video quality metrics like PSNR (Peak Signal-to-Noise Ratio), MSE (Mean Squared Error), SSIM (Structural Similarity Index) are not adequate to evaluate perceptual quality for AI-based compression.

AI codecs often optimize for human perception rather than exact pixel reconstruction, which means a lower PSNR might still look better visually. There's a need for new perceptual quality metrics tailored to ML codecs, capable of assessing:

Temporal consistency
Perceived sharpness
Scene integrity
Task-aware quality (Example: face detection performance in video calls)

Future models may also need adaptive quality scoring, where metrics shift based on content and context.

4. Lack of Standardization

Traditional codecs follow strict standards (Example: H.264/AVC, H.265/HEVC, H.266/VVC etc.), ensuring interoperability between encoders and decoders.

In contrast, AI codecs are not yet standardized.

Each vendor or research group uses custom model architectures and data pipelines. This leads to vendor lock-in and incompatibility: a video encoded by one AI codec cannot be decoded by another unless the exact same model is used.

Without a standardized AI codec framework, widespread adoption across platforms and devices remains a barrier.

5. High Infrastructure and Upgrade Costs

Deploying AI codecs requires major changes across the entire video delivery ecosystem:

Smart TVs, mobile phones, and media players will need hardware upgrades to support real-time ML-based decoding.
Typical device upgrade cycles (It depends on the type of device, for instance, smart TVs are usually upgraded every 2 to 6 years) mean that adoption will be slow, especially for embedded devices.
Cloud-based encoding solutions could offer a stopgap, but increase operational cost and energy consumption.

This high cost of transitioning to AI codec infrastructure, both in terms of compute and compatibility, makes industry-wide rollout a long-term vision rather than an immediate reality.

Conclusion

AI-based video codecs hold the potential to revolutionize video compression by delivering better quality at lower bitrates and adapting to content intelligently. However, challenges around model specialization, hardware acceleration, quality assessment, standardization, and infrastructure cost must be addressed before these codecs can be adopted at scale.

As research continues and hardware evolves, AI codecs could become mainstream by 2030, but overcoming these obstacles will require collaboration between researchers, industry stakeholders, and standards bodies to make it happen.

Disclaimer: The views and opinions expressed in this article are my own and based on personal research and understanding. This content is not affiliated with, endorsed by, or representative of any specific company, organization, or product.

GitHub Actions Deployment Fails with Timeout: How to Troubleshoot

Ramesh Babu Anaparti — Sat, 19 Jul 2025 22:41:22 +0000

GitHub Actions are commonly used for CI/CD pipelines, but deployments can occasionally fail due to timeout errors. This article provides guidance on how to troubleshoot timeout issues in a Kubernetes environment.

Error: timed out waiting for the condition Error: Error: The process 'helm3' failed with exit code 1 Error: The process 'helm3' failed with exit code 1

This error typically occurs when GitHub Actions attempts to deploy a service and something goes wrong, but it doesn't provide specific details about the timeout. To identify the root cause, you'll need to use kubectl to investigate what happened during the deployment.

Below are a few steps you can follow to troubleshoot the failure:

Ensure that you are using the correct Kubernetes context before executing any kubectl commands.

$kubectl config use-context test

1. Inspect Container Status Using kubectl describe

Get the service container details:

$kubectl get pods --all-namespaces -o wide | grep "service-name"
default                      service-name-488d8                    1/1     Running             3          4d7h     10.1.2.2     testnode    <none>           <none>

Get the detailed information about the service resources, status and events:

$kubectl describe pod service-name-488d8 -n default
Name:             service-name-488d8
Namespace:        default
Priority:         0
Service Account:  service-name
Node:             testnode/10.1.2.2
Start Time:       Mon, 14 Jul 2025 12:41:19 -0500
Labels:           app.kubernetes.io/instance=service-name
              app.kubernetes.io/name=service-name
Annotations:      prometheus.io/path: /metrics
              prometheus.io/port: 8000
              prometheus.io/scrape: true
              sidecar.istio.io/inject: false
Status:           Running
IP:               10.1.2.2
IPs:
IP:           10.1.2.2
Controlled By:  ReplicaSet/service-name-488d8
Containers:
service-name:
Container ID:   docker://f12345678a12345678b12345678c12345678d12345678e12345678abcdabcdab
Image:          image-repository.com/service-name:latest
Image ID:       docker-pullable://image-repository.com/service-name@sha256:12345678123456781234567812345678123456781234567812345678abcded12
Ports:          8000/TCP
Host Ports:     8000/TCP
State:          Running
  Started:      Wed, 16 Jul 2025 17:20:20 -0500
Last State:     Terminated
  Reason:       OOMKilled
  Exit Code:    137
  Started:      Tue, 15 Jul 2025 15:14:50 -0500
  Finished:     Wed, 16 Jul 2025 17:20:19 -0500
Ready:          True
Restart Count:  2
Limits:
  memory:  6Gi
Requests:
  memory:   3Gi
Liveness:   http-get http://:http/health/liveness/ delay=0s timeout=1s period=10s #success=1 #failure=3
Readiness:  http-get http://:http/health/readiness/ delay=0s timeout=1s period=10s #success=1 #failure=3
Environment:
  ENV_HOST_NAME:                             (v1:spec.nodeName)
Mounts:
  /service-name/conf from service-name-config (rw)
  /service-name/log from service-name-log (rw)
  /service-name/cores from service-name-cores (rw)
  /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-abcde (ro)
Conditions:
Type              Status
Initialized       True 
Ready             True 
ContainersReady   True 
PodScheduled      True 
Volumes:
service-name-config:
Type:          HostPath (bare host directory volume)
Path:          /host/service-name/conf
HostPathType:  
service-name-log:
Type:          HostPath (bare host directory volume)
Path:          /host/service-name/log
HostPathType:
service-name-cores:
Type:          HostPath (bare host directory volume)
Path:          /host/service-name/cores
HostPathType:  
kube-api-access-abcde:
Type:                    Projected (a volume that contains injected data from multiple sources)
TokenExpirationSeconds:  3607
ConfigMapName:           kube-root-ca.crt
ConfigMapOptional:       
DownwardAPI:             true
QoS Class:                   Burstable
Node-Selectors:              role=server
Tolerations:                 node.kubernetes.io/not-ready:NoExecute op=Exists for 300s
                         node.kubernetes.io/unreachable:NoExecute op=Exists for 300s
Events:
Type     Reason     Age                      From     Message
----     ------     ----                     ----     -------
Warning  Unhealthy  14m (x189 over 3d2h)     kubelet  Liveness probe failed:
Warning  Unhealthy  6m38s (x182 over 2d23h)  kubelet  Readiness probe failed:

Inspect Pod Status
Check the pod's last state, including the reason and exit code. The container might have been OOMKilled, crashed, or restarted for another reason.
Review Events
Look for warnings or failures related to scheduling, health checks, or image pulls.
Check Health Probes
If liveness or readiness probes are failing, investigate why the health check endpoints are returning unhealthy. This could indicate issues in application startup, configuration, or dependencies.
Verify Image Pull Status
If the pod is stuck in ImagePullBackOff or ErrImagePull, it might be unable to download the image due to:
- Incorrect image reference or missing image in the repository
- Authentication issues
- Large image size causing timeout or resource constraints
Verify Environment Configuration
- Incorrect or missing environment variables, configuration files, or file paths can also prevent the application from starting or cause runtime crashes.

2. Review the container logs

$kubectl logs service-name-488d8 --tail=100

Analyze container logs for error patterns or stack traces.
If supported, enable additional logging dynamically and monitor for any issues.

3. Crashlooping container

$kubectl get pods --all-namespaces -o wide| grep "service-name"
default                      service-name-488d8                    1/1        Running             3          4d7h     10.1.2.2     testnode    <none>           <none>
default                      service-name-567e9                    0/1        CrashLoopBackOff    22         98m      10.1.2.2     testnode    <none>           <none>

One of the containers "service-name-567e9", which was intended to deploy and replace the previous one, is in a CrashLoopBackOff state. This container requires analysis or debugging to determine the root cause. - Debugging a crash-looping container can be challenging. To investigate further, shell into the node where the service is running and modify the container to run in sleep mode. This allows you to access the container for deeper analysis. You can then run tools like gdb to perform debugging.

testnode / # docker ps | grep "service-name"
123456789abc   eabcd123456e                                                     "/usr/bin/dumb-init …"    17 minutes ago   Up 17 minutes             k8s_service-name_service-name-b1234abc4-488d8_default_f2a8d5f7-f03f-4944-9cbf-1bf43f2d8881_5
18b1b5f76d02   k8s.gcr.io/pause:3.4.1                                           "/pause"                  5 days ago       Up 5 days                 k8s_POD_service-name-b1234abc4-488d8_default_f2a8d5f7-f03f-4944-9cbf-1bf43f2d8881_0
testnode / # docker exec -it 123456789abc bash
testnode:/app [main]$

Absence of a Persistent Foreground Process: Docker containers need a foreground process to stay running. If the main application process exits, or if the CMD or ENTRYPOINT in the Dockerfile doesn’t keep a process active, the container will automatically stop and restart, which cause crashlooping. Make sure your foreground process is actively running.