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Python Dynamic Image Quality Example
import cStringIO
import PIL.Image
from ssim import compute_ssim
def get_ssim_at_quality(photo, quality):
"""Return the ssim for this JPEG image saved at the specified quality"""
ssim_photo = cStringIO.StringIO()
# optimize is omitted here as it doesn't affect
# quality but requires additional memory and cpu, format="JPEG", quality=quality, progressive=True)
ssim_score = compute_ssim(photo,
return ssim_score
def _ssim_iteration_count(lo, hi):
"""Return the depth of the binary search tree for this range"""
if lo >= hi:
return 0
return int(log(hi - lo, 2)) + 1
def jpeg_dynamic_quality(original_photo):
"""Return an integer representing the quality that this JPEG image should be
saved at to attain the quality threshold specified for this photo class.
original_photo - a prepared PIL JPEG image (only JPEG is supported)
ssim_goal = 0.95
hi = 85
lo = 80
# working on a smaller size image doesn't give worse results but is faster
# changing this value requires updating the calculated thresholds
photo = original_photo.resize((400, 400))
if not _should_use_dynamic_quality():
default_ssim = get_ssim_at_quality(photo, hi)
return hi, default_ssim
# 95 is the highest useful value for JPEG. Higher values cause different behavior
# Used to establish the image's intrinsic ssim without encoder artifacts
normalized_ssim = get_ssim_at_quality(photo, 95)
selected_quality = selected_ssim = None
# loop bisection. ssim function increases monotonically so this will converge
for i in xrange(_ssim_iteration_count(lo, hi)):
curr_quality = (lo + hi) // 2
curr_ssim = get_ssim_at_quality(photo, curr_quality)
ssim_ratio = curr_ssim / normalized_ssim
if ssim_ratio >= ssim_goal:
# continue to check whether a lower quality level also exceeds the goal
selected_quality = curr_quality
selected_ssim = curr_ssim
hi = curr_quality
lo = curr_quality
if selected_quality:
return selected_quality, selected_ssim
default_ssim = get_ssim_at_quality(photo, hi)
return hi, default_ssim
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