Video Upscaling + HEVC Encoding Optimization (PieAPP-driven quality)

@AwaisChaudhry

Hello, I'm Muhammad Awais. I will build a fast, high-quality 2× and 4× video upscaling pipeline with PieAPP-focused evaluation and robust HEVC encoding tuning. My approach has two tracks: 1) upscaling model and training: a compact perceptual-upscaling network designed for real-time inference, trained with a loss that combines pixel accuracy and PieAPP-aligned perceptual error, plus data augmentation and efficient inference paths. We'll run staged training to trade off speed and quality, validate with a 10-frame PieAPP sample, and deliver drop-in scripts to reproduce results. 2) FFmpeg encoding optimization: a per-video adaptive CRF/maxrate strategy using libx265 and/or hevc_nvenc, VBV tuning, AQ/psy tuning, and a lightweight evaluation loop to choose per-clip parameters that minimize perceptual error. We'll provide end-to-end reproducible configs, benchmark scripts, and a minimal integration layer to plug into your pipeline. The result is a practical, reproducible setup that meets 10–15 FPS on your ~300-frame test and provides clear guidance for production. Which hardware/deployment target should I optimize for (CPU vs GPU, specific GPUs), and what PieAPP threshold should guide the optimization? Best regards,

@LiveExperts

Hello, As an engineer specializing in computer vision and with proficiency in Python, I believe I am the ideal candidate for your project. My competence extends to various fields that hold relevance to your needs - Machine Learning, Deep Learning, Image Processing, and more crucially, FFmpeg encoding. My existing knowledge of encoding strategies, such as rate-control and VBV would be a valuable asset for optimizing the HEVC encoding process you require. A significant part of my experience has been focused on image enhancement similar to the video upscaling challenges you describe. I have previously designed strategies involving strict speed constraints, not unlike the one presented here with your 2×/4× upscaling necessity. In these projects as well, performance improvement was appraised using perceptual metrics just as you intend to utilize PieAPP. Additionally, I have excelled in completing experiment-based projects like yours with a commitment to reproducibility. Be it developing a script or creating drop-in-configs – you can count on me for an organized and functional deliverable. You can explore our GitHub portfolio [include link] for validating our workmanship. Given my proficiency in engineering tools relevant to this project like OpenCV and TensorFlow combined with a touch of creativity, I assure you an end-to-end solution that will enhance your pipeline significantly. Thanks!

@ZohaibRoy

⭐⭐⭐⭐⭐ Enhance Video Upscaling and Encoding for Optimal Quality ❇️ Hi My Friend, I hope you're doing well. I reviewed your project needs and see you're looking for an expert in video upscaling and FFmpeg encoding. You don't need to look any further; Zohaib is here to assist you! My team has completed over 50 similar projects, focusing on video quality enhancement. I can improve your upscaling model and FFmpeg encoding strategy to achieve the best perceptual quality. ➡️ Why Me? I have 5 years of experience in video processing, specializing in upscaling and encoding. My skills include FFmpeg, perceptual metrics, and real-world video content optimization. I also have a strong grip on machine learning techniques that can enhance your pipeline effectively. ➡️ Let's have a quick chat to discuss your project in detail. I will share samples of my previous work that demonstrate my expertise in video upscaling and encoding. Looking forward to discussing this with you. ➡️ Skills & Experience: ✅ FFmpeg ✅ libx265 ✅ hevc_nvenc ✅ Perceptual Metrics ✅ Video Upscaling ✅ Adaptive CRF ✅ Rate Control ✅ VBV ✅ Experiment Design ✅ Quality Optimization ✅ Video Processing ✅ Real-time Encoding Waiting for your response! Best Regards, Zohaib

@irsolutions

With my extensive experience in video upscaling, HEVC encoding optimization, and strong FFmpeg knowledge, I understand the complexities of your project requirements. The need to improve the upscaling model/training strategy for perceptual quality and optimize the FFmpeg HEVC encoding strategy align perfectly with my expertise. In the realm of video SR/upscaling training and inference optimization, I have successfully implemented similar solutions that strike the perfect balance between speed and quality. My experience with perceptual metrics and "optimize for the metric" workflows, including PieAPP, ensures that the final output meets your expectations. For your project, I will leverage my skills to create reproducible experiments and deliver configurations that seamlessly integrate into your pipeline. My track record of delivering high-quality video solutions in various domains, combined with my understanding of adaptive parameters and encoding strategies, positions me as the ideal candidate for this task. I am eager to showcase my capabilities by providing 1–2 examples of similar work and addressing the screening questions to demonstrate my competence in tackling the challenges presented by your project. Let's collaborate to bring your vision to life and achieve exceptional results together.

@chraheelbhatti

As a highly skilled and experienced Machine Learning engineer with a direct focus on Computer Vision and Cloud-based AI solutions, I have built an extensive portfolio that is directly related to your project. My experience at State Institutions, Unilever Pakistan, and State Bank of Pakistan working on voice recognition systems, surveillance applications, and automated workflows will be of great value to this project. In conclusion, my distinguished background in Machine Learning research, hands-on experience in Computer Vision projects and proficiency in HEVC encoding utilizing FFmpeg gateways attribute me as the ideal candidate to optimize your pipeline. My innovative approaches have always produced efficient systems that are both speed-focused without compromising quality—precisely what your project demands. I am committed to delivering high-quality outputs within deadlines while adhering strictly to client briefs. Let's turn your vision into reality!

@dvcontact

Hello, Interested in transforming your video upscaling process to achieve unmatched quality and performance? I specialize in optimizing video upscaling and HEVC encoding strategies, leveraging cutting-edge metrics like PieAPP for superior perceptual outcomes. Let's connect to explore how I can enhance your pipeline and exceed your performance targets. Best, Smith

@Julian29

Hello I’m a video processing & deep learning engineer with 10+ years of experience, specializing in super-resolution, perceptual quality optimization, and FFmpeg/HEVC pipelines. I can improve your 2×/4× upscaling workflow with PieAPP-driven quality evaluation while optimizing FFmpeg HEVC encoding (libx265 / hevc_nvenc) for adaptive CRF/maxrate selection. Relevant Expertise: Video SR training and inference for real-world content (sports, night scenes, animals, travel) Perceptual loss optimization targeting PieAPP, VMAF, LPIPS, SSIM FFmpeg/libx265 encoding: CRF, VBV, maxrate, AQ/psy tuning High-performance pipelines: 10–15 FPS+ for 300-frame clips Screening Answers (summary): Upscaling Model: Lightweight ESRGAN / Real-ESRGAN variants or SwinIR optimized for speed; use a combination of L1 + perceptual (VGG) + PieAPP proxy loss for perceptual fidelity. Data augmentation on real-world video patches improves generalization. HEVC Adaptive CRF/Maxrate: Use a fast sampling of key frames + perceptual metric evaluation to pick per-video CRF/maxrate. Optionally, segment-wise evaluation with dynamic CRF selection, constrained by VBV buffer. Heuristic + small search is faster than brute-force. I can deliver reproducible scripts/configs that are drop-in ready for your pipeline. Portfolio and prior experiments available on request. Thanks.

@yuliussmayoru

Hi! This is a dream project for anyone serious about perceptual quality in video processing. I bring experience with both real-time upscaling pipelines and tuning HEVC encoders for visual metrics—especially in situations where “lower CRF = better quality” doesn’t always hold up. I’ve worked on similar pipelines where inference-time constraints and perceptual metrics (PieAPP, LPIPS, VMAF) drove both model design and encoding logic. I’m happy to share a detailed example or past project if you’d like. Are you already locked into a specific SR model base or open to switching if it improves speed/quality? Best Regards, Yulius Mayoru...

@Specgi

Drawing on a diverse set of skills, my name is Usman Haider, and I am the ideal fit for your video upscaling project. With a solid background in artificial intelligence and machine learning, I have extensive experience implementing intricate ML models tailored for specific tasks - precisely what you require for the upscaling model. In particular, I've successfully designed systems to optimize for perceptual quality using custom loss functions, which translate well to your requirements with PieAPP-style metrics. I would approach the task by leveraging established SR/upscaling techniques such as deep learning-based frameworks like ESRGAN or SRResNet while implementing loss functions and configurations that optimize specifically for PieAPP-style perceptual error. In terms of video analysis and compression, my expertise with FFmpeg (libx265 and/or hevc_nvenc) and rate-control, VBV and AQ/psy tuning will prove invaluable. For instance, in relation to your adaptive CRF and maxrate problem, I've dealt with previous instances where "lower CRF is better" failed by isolating and handling them separately either by segment or region within a video. This way, we can reliably choose parameters that minimize perceptual errors while still ensuring high overall quality- an optimal 'best of both worlds', so to speak.

@realroshan102

Hi, Your project on building a high-performance video upscaling pipeline evaluated by PieAPP intrigued me deeply. I have extensive experience working with FFmpeg (libx265, hevc_nvenc) and optimizing rate-control and encoding parameters to balance quality and performance. I am skilled in perceptual quality-driven upscaling models and training workflows, having optimized SR models alongside perceptual metrics like VMAF and LPIPS. I will enhance your 2×/4× upscaling model to prioritize PieAPP reductions and develop adaptive HEVC encoding schemes for precise CRF and maxrate tuning per video, ensuring reproducible, drop-in scripts for seamless integration. My approach guarantees meeting your performance target of 10–15 FPS while maintaining top perceptual quality. I propose starting with a detailed review of your current pipeline and adapting training losses and encoding controls based on PieAPP feedback loops, aiming for rapid iteration within 14 days. Could you share more about your current upscaling model architecture and training setup, as well as your existing method for adaptive CRF/maxrate selection? Thanks, Roshan

@revival786

Greetings! I’m a top-rated freelancer with 16+ years of experience and a portfolio of 750+ satisfied clients. I specialize in delivering high-quality, professional video upscaling + HEVC encoding optimization services tailored to your unique needs. Please feel free to message me to discuss your project and review my portfolio. I’d love to help bring your ideas to life! Looking forward to collaborating with you! Best regards, Revival

@ProfessorVats

I am an expert in video upscaling and HEVC encoding optimization, with a proven track record in enhancing perceptual quality using PieAPP. My strengths lie in developing efficient upscaling models and fine-tuning FFmpeg parameters for optimal performance. I offer 24/7 support and am eager to discuss how I can deliver results tailored to your project needs. Let’s connect!

@invoketech

Hello!! I understand your goal of building a high-performance 2×/4× video upscaling pipeline optimized explicitly for PieAPP, covering both SR model design/training and HEVC encoding parameter selection where perceptual quality is non-monotonic. I have 10+ years of experience working with video processing pipelines, super-resolution models (Real-ESRGAN/EDSR/SwinIR variants), perceptual metrics (PieAPP, LPIPS, VMAF), and FFmpeg HEVC tuning (libx265 + NVENC) including CRF, VBV, AQ, psy-RD, and content-adaptive rate control. My approach focuses on metric-driven optimization, reproducible experiments, and fast inference (TensorRT/FP16, tiling) to consistently meet 10–15+ FPS targets. I will deliver drop-in configs, scripts, and documented experiment results that reliably select CRF/maxrate per video or segment based on perceptual probes rather than assumptions. WORKING FLOW (TARGETED & PRACTICAL) Model Selection & Baseline → Choose fast SR architecture (2×/4×) and establish PieAPP baseline. Perceptual Loss Design → Tune loss stack (LPIPS-style, feature-space, edge bias) aligned with PieAPP behavior. Inference & Speed Tuning → Optimize model + runtime to meet 10–15 FPS without perceptual collapse. Encoding Sweep & Analysis → Run controlled CRF/VBV/AQ sweeps to map PieAPP response per content class. Adaptive Parameter Logic → Build reproducible script to select CRF/maxrate per clip or segment. I eagerly await your positive response. Thanks, InvokeTech

@mobasherreza10

Hi there, With over a decade of experience in multiple facets of technology, I have developed an extensive skillset that aligns perfectly with your project needs. Specifically, my expertise in AI and Machine Learning includes Image and Video Processing using PyTorch and TensorFlow. I have used both for image and video upscaling processes. In addition, I possess comprehensive knowledge in FFmpeg and have the ability to design highly efficient encoding strategies that can optimize not only for quality but also for perceptual error. To ensure I build a reliable upscaling model for you within speed constraints, I would employ deep neural networks like SRCNN or EDSR alongside PieAPP-style loss functions. This would result in far higher perceptual quality when upscaling images or videos under strict time limitations, much like the ones your project requires. In terms of choosing CRF and/or maxrate per video to minimize perceptual error in encoding, I can adopt adaptive approaches based on statistical analysis rather than mere monotonous quality attributes, effectively addressing the paradoxical situations you mentioned above. Additionally, I have ample experience conducting comprehensive experiments and delivering "drop-in" configuration/scripts as requested, ensuring high repeatability in different settings. Best regards, Mobasher Reza

@nmans

As someone experienced in developing optimized solutions for resource-intensive tasks, I'm confident that I'm the right fit for your video upscaling and encoding project. My python expertise, especially in FFmpeg (libx265 and/or hevc_nvenc), can be effectively leveraged towards enhancing your pipeline's ability to handle ~300 frames in 20–30 seconds while improving perceptual quality. With a deep understanding of perceptual metrics such as PieAPP, VMAF, LPIPS, and SSIM, I'm no stranger to "optimize for the metric" workflows. In fact, I've previously incorporated such workflows in my projects (examples provided on Github) which have successfully demonstrated precision improvement with downscaled versions as well. Moreover, my skills extend to reproducible experimentation and delivering "drop-in" configs/scripts which would address your need for an efficient workflow. Given my solid grasp on not only the technicalities of the task but also on intricate aesthetic demands across different video domains like sports, night scenes, animals, travel etc. , I believe with me onboard you can maximize your project's quality potential while optimising speed-specific constraints. Looking forward to optimizing your project with you!

@novatech2210

Hi June, Just wrapped up a similar FFmpeg project – a high-performance video encoding pipeline for HEVC (libx265 and hevc_nvenc) encoding with adaptive CRF/maxrate selection for a major streaming platform. Exactly the level of expertise you're looking for. We’re the perfect fit for this. I specialize in building powerful FFmpeg solutions using rate-control, VBV (maxrate/bufsize), AQ/psy tuning, and experience with perceptual metrics like PieAPP, VMAF, LPIPS, and SSIM. I've worked extensively on video SR/upscaling training and inference optimization, always keeping in mind the speed/quality trade-offs. Multiple 5-star reviews on complex video encoding and upscaling projects. I've also optimized HEVC encoding for various use cases, including VBR and CBR scenarios. Happy to jump on a quick call to discuss your setup and answer the screening questions. Worst case, you get a free consultation and some solid ideas. Chris | Lead Developer | Novatech

@temoberishvili

Hello, I specialize in high-performance video upscaling and encoding optimization. I can help design a pipeline that maximizes perceptual quality while keeping end-to-end throughput high (≥10–15 FPS for ~4K output). My experience spans super-resolution model training, perceptual metric optimization (PieAPP, VMAF, LPIPS), and fine-tuning HEVC encoding parameters using both libx265 and hevc_nvenc, including rate-control, maxrate/bufsize, AQ/psy tuning, and segment-level adjustments. I also deliver reproducible scripts/configs suitable for drop-in integration.

@melissap68

This is exactly the kind of work I enjoy doing — projects like this combine precision and creativity, and I make sure both show through in every detail. With a strong background in FFmpeg, perceptual metrics, and video upscaling optimization, I can enhance quality while meeting speed targets. I consistently deliver high-quality results, effective communication, and a smooth process. Happy to offer insights regardless of your decision. Kind regards, Melissa Pringle.

@SahseeTechnology

Hello, I can help you systematically lower PieAPP scores by tackling both sides of your pipeline: SR model/training for perceptual quality and HEVC encoding strategy where “lower CRF ≠ better perceptual quality.” I’ve worked on metric-driven optimization loops (PieAPP/VMAF/LPIPS) where success depends on measuring what actually correlates with perception, not just pushing bitrate or PSNR. How I’d approach this project 1) Upscaling model & training For strict speed targets (10–15 FPS+), I’d use a lightweight CNN/Transformer hybrid (e.g., optimized ESRGAN-variant or SwiftSR/Real-ESRGAN-style backbone with pruning/quantization). Training would prioritize perceptual loss stacks: PieAPP proxy (via LPIPS + feature-space losses), adversarial loss tuned conservatively, and content-adaptive weighting for noise, texture, and dark scenes. I focus on avoiding over-sharpening, which often hurts PieAPP. 2) HEVC encoding & adaptive selection I do metric-guided CRF/VBV sweeps on representative frame samples and train a simple regressor or rule-based selector using scene features (motion, noise, texture energy). This avoids the “CRF 10 paradox.” Per-segment CRF/maxrate selection is achievable without brute force. Deliverables • Reproducible experiments • Drop-in FFmpeg configs/scripts • Clear decision logic for CRF/maxrate selection • Speed/quality tradeoff analysis Happy to discuss next steps. Thanks, Gagan Jeet Singh