import torchimport torchvision.transforms as transformsfrom torch.utils.data import DataLoader, Datasetimport requestsimport jsonfrom typing import List, Tupleimport cv2import numpy as npclass SimaBitVideoDataset(Dataset):    """    PyTorch Dataset that integrates SimaBit API preprocessing    for on-the-fly video compression during training.    """        def __init__(self, video_paths: List[str], api_key: str,                  target_resolution: Tuple[int, int] = (224, 224)):        self.video_paths = video_paths        self.api_key = api_key        self.target_resolution = target_resolution        self.transform = transforms.Compose([            transforms.ToPILImage(),            transforms.Resize(target_resolution),            transforms.ToTensor(),            transforms.Normalize(mean=[0.485, 0.456, 0.406],                                std=[0.229, 0.224, 0.225])        ])        def __len__(self):        return len(self.video_paths)        def preprocess_with_simabit(self, video_path: str) -> np.ndarray:        """        Call SimaBit API for video preprocessing before loading.        """        # Read original video        cap = cv2.VideoCapture(video_path)        frames = []                while True:            ret, frame = cap.read()            if not ret:                break            frames.append(frame)                cap.release()                # Prepare API request        api_url = "https://api.sima.live/v1/preprocess"        headers = {            "Authorization": f"Bearer {self.api_key}",            "Content-Type": "application/json"        }                # Convert frames to base64 for API transmission        encoded_frames = []        for frame in frames:            _, buffer = cv2.imencode('.jpg', frame)            encoded_frame = base64.b64encode(buffer).decode('utf-8')            encoded_frames.append(encoded_frame)                payload = {            "frames": encoded_frames,            "compression_target": 0.75,  # 25% compression            "preserve_ssim": True,            "output_format": "numpy"        }                # Make API call        response = requests.post(api_url, headers=headers, json=payload)                if response.status_code == 200:            result = response.json()            # Decode preprocessed frames            preprocessed_frames = []            for encoded_frame in result['preprocessed_frames']:                frame_data = base64.b64decode(encoded_frame)                frame = cv2.imdecode(np.frombuffer(frame_data, np.uint8), cv2.IMREAD_COLOR)                preprocessed_frames.append(frame)                        return np.array(preprocessed_frames)        else:            # Fallback to original frames if API fails            return np.array(frames)        def __getitem__(self, idx):        video_path = self.video_paths[idx]                # Preprocess with SimaBit        frames = self.preprocess_with_simabit(video_path)                # Apply PyTorch transforms        processed_frames = []        for frame in frames:            # Convert BGR to RGB            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)            tensor_frame = self.transform(frame_rgb)            processed_frames.append(tensor_frame)                # Stack frames into tensor        video_tensor = torch.stack(processed_frames)                return video_tensor, idx# Usage exampledef create_simabit_dataloader(video_paths: List[str], api_key: str,                              batch_size: int = 4, num_workers: int = 4):    """    Create a DataLoader with SimaBit preprocessing integration.    """    dataset = SimaBitVideoDataset(video_paths, api_key)    dataloader = DataLoader(        dataset,         batch_size=batch_size,         shuffle=True,         num_workers=num_workers,        pin_memory=True    )    return dataloader# Training loop integrationdef train_with_simabit_preprocessing(model, dataloader, optimizer, device):    """    Training loop that uses SimaBit-preprocessed video data.    """    model.train()    total_loss = 0        for batch_idx, (video_batch, indices) in enumerate(dataloader):        video_batch = video_batch.to(device)                # Forward pass        optimizer.zero_grad()        outputs = model(video_batch)                # Calculate loss (example for autoencoder)        loss = torch.nn.functional.mse_loss(outputs, video_batch)                # Backward pass        loss.backward()        optimizer.step()                total_loss += loss.item()                if batch_idx % 100 == 0:            print(f'Batch {batch_idx}, Loss: {loss.item():.6f}')        return total_loss / len(dataloader)

# Benchmark results comparisonbenchmark_results = {    'baseline': {        'convergence_epochs': 45,        'final_accuracy': 0.847,        'training_time_hours': 72,        'storage_gb': 2400    },    'simabit_compressed': {        'convergence_epochs': 46,        'final_accuracy': 0.844,        'training_time_hours': 68,        'storage_gb': 1800    }}# Calculate efficiency metricsdef calculate_efficiency_metrics(results):    baseline = results['baseline']    compressed = results['simabit_compressed']        storage_savings = (baseline['storage_gb'] - compressed['storage_gb']) / baseline['storage_gb']    time_savings = (baseline['training_time_hours'] - compressed['training_time_hours']) / baseline['training_time_hours']    accuracy_retention = compressed['final_accuracy'] / baseline['final_accuracy']        return {        'storage_savings_pct': storage_savings * 100,        'time_savings_pct': time_savings * 100,        'accuracy_retention_pct': accuracy_retention * 100    }efficiency = calculate_efficiency_metrics(benchmark_results)print(f"Storage Savings: {efficiency['storage_savings_pct']:.1f}%")print(f"Training Time Reduction: {efficiency['time_savings_pct']:.1f}%")print(f"Accuracy Retention: {efficiency['accuracy_retention_pct']:.1f}%")

import asyncioimport aiohttpfrom concurrent.futures import ThreadPoolExecutorimport boto3from typing import AsyncGeneratorclass DistributedSimaBitProcessor:    """    Distributed processing system for large-scale video preprocessing    with SimaBit integration.    """        def __init__(self, api_key: str, s3_bucket: str, max_concurrent: int = 10):        self.api_key = api_key        self.s3_bucket = s3_bucket        self.s3_client = boto3.client('s3')        self.semaphore = asyncio.Semaphore(max_concurrent)        self.session = None        async def __aenter__(self):        self.session = aiohttp.ClientSession()        return self        async def __aexit__(self, exc_type, exc_val, exc_tb):        if self.session:            await self.session.close()        async def process_video_batch(self, video_keys: List[str]) -> AsyncGenerator[str, None]:        """        Process a batch of videos asynchronously with SimaBit preprocessing.        """        tasks = [self.process_single_video(key) for key in video_keys]                for completed_task in asyncio.as_completed(tasks):            try:                result = await completed_task                yield result            except Exception as e:                print(f"Error processing video: {e}")                continue        async def process_single_video(self, video_key: str) -> str:        """        Process a single video with SimaBit preprocessing.        """        async with self.semaphore:            # Download video from S3            local_path = f"/tmp/{video_key.split('/')[-1]}"            self.s3_client.download_file(self.s3_bucket, video_key, local_path)                        # Process with SimaBit API            processed_path = await self.call_simabit_api(local_path)                        # Upload processed video back to S3            processed_key = f"processed/{video_key}"            self.s3_client.upload_file(processed_path, self.s3_bucket, processed_key)                        # Cleanup local files            os.remove(local_path)            os.remove(processed_path)                        return processed_key        async def call_simabit_api(self, video_path: str) -> str:        """        Async call to SimaBit API for video preprocessing.        """        api_url = "https://api.sima.live/v1/preprocess"        headers = {            "Authorization": f"Bearer {self.api_key}",            "Content-Type": "application/octet-stream"        }                with open(video_path, 'rb') as video_file:            video_data = video_file.read()                async with self.session.post(api_url, headers=headers, data=video_data) as response:            if response.status == 200:                processed_data = await response.read()                processed_path = video_path.replace('.mp4', '_processed.mp4')                                with open(processed_path, 'wb') as output_file:                    output_file.write(processed_data)                                return processed_path            else:                raise Exception(f"API call failed with status {response.status}")# Usage example for large-scale processingasync def process_openvid_dataset(video_keys: List[str], api_key: str):    """    Process the entire OpenVid-1M dataset with distributed SimaBit preprocessing.    """    async with DistributedSimaBitProcessor(api_key, 'openvid-dataset') as processor:        processed_count = 0                async for processed_key in processor.process_video_batch(video_keys):            processed_count += 1            if processed_count % 100 == 0:                print(f"Processed {processed_count} videos")                print(f"Total processed: {processed_count} videos")

import cv2import numpy as npfrom skimage.metrics import structural_similarity as ssimfrom typing import Dict, List, Tupleclass VideoQualityMonitor:    """    Comprehensive quality monitoring for SimaBit-processed videos.    """        def __init__(self, quality_thresholds: Dict[str, float] = None):        self.thresholds = quality_thresholds or {            'ssim_min': 0.95,            'psnr_min': 35.0,            'mse_max': 100.0        }        self.quality_history = []        def calculate_ssim(self, original: np.ndarray, compressed: np.ndarray) -> float:        """        Calculate SSIM between original and compressed frames.        """        # Convert to grayscale if needed        if len(original.shape) == 3:            original_gray = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)            compressed_gray = cv2.cvtColor(compressed, cv2.COLOR_BGR2GRAY)        else:            original_gray = original            compressed_gray = compressed                return ssim(original_gray, compressed_gray)        def calculate_psnr(self, original: np.ndarray, compressed: ## Frequently Asked Questions### What is the OpenVid-1M dataset and why is it challenging to store?The OpenVid-1M dataset is a cornerstone resource for training generative AI video models like OpenAI Sora. It contains terabytes of high-resolution 1080p video clips, creating enormous storage and bandwidth challenges for AI researchers and companies due to the massive computational requirements for text-to-video model training.### How does SimaBit's AI-powered preprocessing improve video compression?SimaBit uses AI-powered preprocessing to optimize video content before compression, analyzing each frame to determine the best compression parameters without sacrificing quality. This approach addresses the limitations of traditional compression methods by intelligently adapting to video content characteristics, similar to how AI video codecs can reduce bandwidth requirements for streaming applications.### What are the main limitations of traditional video compression methods for GenAI datasets?Traditional compression methods struggle with GenAI datasets because they use fixed compression parameters that don't adapt to varying content complexity. They often result in quality loss or inefficient compression ratios when dealing with the diverse visual content found in large-scale AI training datasets like OpenVid-1M.### How does content-adaptive compression technology work in video encoding?Content-adaptive compression technology, like Beamr's CABR, modifies encoding parameters per frame using patented quality measures. It selects the best candidate frame with the lowest bitrate while maintaining the same perceptual quality as the original, potentially reducing bitrates by up to 50% compared to traditional encoding methods.### What role does AI play in modern video quality enhancement and compression?AI analyzes video content in real-time to predict optimal compression settings and enhance visual details frame by frame. Machine learning algorithms can reduce pixelation, restore missing information in low-quality videos, and dynamically adjust compression parameters based on content complexity and network conditions for optimal viewing experience.### How significant are the compression improvements with next-generation codecs like VVC?Next-generation codecs like h.266/VVC promise significant improvements over predecessors, with Fraunhofer HHI claiming that VVC can improve visual quality and reduce bitrate expenditure by around 50% compared to h.265/HEVC. This represents a major advancement for organizations in the streaming industry dealing with large-scale video content.## Sources1. [https://arxiv.org/abs/1908.00812?context=cs.MM](https://arxiv.org/abs/1908.00812?context=cs.MM)2. [https://beamr.com/cabr](https://beamr.com/cabr)3. [https://bitmovin.com/ai-video-research](https://bitmovin.com/ai-video-research)4. [https://www.forasoft.com/blog/article/ai-video-quality-enhancement](https://www.forasoft.com/blog/article/ai-video-quality-enhancement)5. [https://www.sima.live/](https://www.sima.live/)6. [https://www.sima.live/blog/understanding-bandwidth-reduction-for-streaming-with-ai-video-codec](https://www.sima.live/blog/understanding-bandwidth-reduction-for-streaming-with-ai-video-codec)