# Copyright 2026 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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"""
The reward function for JPEG compressibility.
It is adapted from https://github.com/kvablack/ddpo-pytorch.
"""
import io
import numpy as np
import torch
from PIL import Image
def jpeg_incompressibility():
def _fn(images, prompts):
if isinstance(images, torch.Tensor):
images = (images * 255).round().clamp(0, 255).to(torch.uint8).cpu().numpy()
images = images.transpose(0, 2, 3, 1) # NCHW -> NHWC
images = [Image.fromarray(image) for image in images]
buffers = [io.BytesIO() for _ in images]
for image, buffer in zip(images, buffers, strict=False):
image.save(buffer, format="JPEG", quality=95)
sizes = [buffer.tell() / 1000 for buffer in buffers]
return np.array(sizes), {}
return _fn
def jpeg_compressibility():
jpeg_fn = jpeg_incompressibility()
def _fn(images, prompts):
rew, meta = jpeg_fn(images, prompts)
return -rew / 500, meta
return _fn
[docs]
def compute_score(solution_image):
"""The scoring function for JPEG compressibility.
Args:
solution_image: the solution image or video, in shape (C, H, W) or (N, C, H, W).
"""
if isinstance(solution_image, torch.Tensor) and solution_image.ndim == 3:
solution_image = solution_image.unsqueeze(0)
score = jpeg_compressibility()(solution_image, None)[0]
return score