Imam Ainudin Pirmansah, Dhimas Adi Satria, Rifai Ahmad Musthofa

ANALISA STRATEGIS DALAM PROSES GENERATE IMAGE-TO-VIDEO PADA PLATFORM AI GENERATIF UNTUK OPTIMALISASI KUALITAS VIDEO

Introduction

Analisa strategis dalam proses generate image-to-video pada platform ai generatif untuk optimalisasi kualitas video. Teliti platform AI generatif image-to-video (Runway, Kling, Pixverse, Hailuo) untuk optimasi kualitas video. Dapatkan panduan praktis memilih platform terbaik sesuai kebutuhan kreatif Anda.

Abstract

Perkembangan pesat kecerdasan buatan generatif (AI) telah mentransformasi proses pembuatan konten digital, khususnya dalam menghasilkan video dari gambar statis. Meskipun telah hadir berbagai platform AI image-to-video seperti Kling, Runway, Pixverse, dan Hailuo, hingga kini belum ada benchmarking komprehensif dan sistematis terkait kinerja serta kualitas output dari platform-platform tersebut. Penelitian ini menyajikan analisis komparatif terhadap keempat platform guna mengoptimalkan kualitas video untuk kebutuhan kreatif maupun profesional. Pendekatan kuantitatif dan kualitatif digunakan melalui pengukuran waktu proses, resolusi, frame rate, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), serta penilaian berbasis skala Likert oleh panelis ahli. Hasil eksperimen menunjukkan bahwa Runway secara konsisten menghasilkan kualitas visual dan sinematografi terbaik, Kling unggul dalam stabilitas karakter dan efisiensi biaya, Hailuo menonjol pada detail tekstur, sementara Pixverse menawarkan workflow tercepat. Temuan ini memberikan panduan praktis bagi pengguna, pengembang, dan pelaku industri dalam memilih dan mengoptimalkan platform AI image-to-video.

Review

This paper addresses a highly pertinent and rapidly evolving area within generative artificial intelligence: the transformation of static images into dynamic video content. The authors accurately identify a critical gap in the existing literature and practical application, noting the absence of systematic and comprehensive benchmarking for prevalent AI image-to-video platforms such as Kling, Runway, Pixverse, and Hailuo. The study's stated objective to conduct a comparative analysis to optimize video quality for creative and professional needs is both timely and valuable, promising to fill this crucial knowledge void. The methodology employed by the researchers is commendable, integrating both quantitative and qualitative approaches to ensure a robust evaluation. Key performance indicators included technical metrics such as processing time, resolution, frame rate, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Supplementing these objective measures, a qualitative assessment by expert panelists utilizing a Likert scale provides a holistic view of perceived quality. The experimental results reveal distinct strengths among the platforms: Runway consistently excelled in overall visual and cinematographic quality, Kling demonstrated superior character stability and cost efficiency, Hailuo stood out for its intricate texture details, and Pixverse offered the fastest workflow. The findings presented in this research carry significant practical implications for a broad spectrum of stakeholders, including individual users, platform developers, and industry professionals. By clearly delineating the performance characteristics and optimal use cases for each platform, the study effectively serves as a practical guide for informed decision-making in selecting and leveraging AI image-to-video tools. This systematic comparative analysis, employing a rigorous mixed-methods approach, represents a valuable contribution to the field, offering clarity in a landscape characterized by rapid technological advancement and diverse options. It lays a solid foundation for optimizing content creation workflows and advancing the quality of AI-generated video.

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